Practice Exams:

View All

Microsoft Fabric Analytics Engineer DP-600 Data Connections Discovery Ingestion And Stores Practice Test

Skill 2.1 – 75 original questions

This Microsoft DP-600 practice test focuses on data connections discovery ingestion and stores through original scenario-based questions aligned to the active DP-600 skills measured as of July 21, 2026. Use the complete ExamSnap DP-600 collection for broader practice across Microsoft Fabric analytics lifecycle, data preparation, querying, and semantic modeling. For broader exam preparation, review the Microsoft DP-600 Exam Dumps page.

Instructions: Select the best answer for each question. Review the explanation after answering; each option includes a reason it is or is not the strongest choice for the scenario.

Question 1

Tailspin Toys has a change request for the marketing semantic model: reuse governed credentials for a cloud source across Fabric items. The enterprise reporting group wants a solution where the implementation should be easy to troubleshoot. Which implementation is most suitable? Existing users should keep their current access.

Precompute the required daily aggregate table or result
Append or union the row sets instead of joining them
Use an inner join on the matching business or surrogate key
Use a DAX query with functions such as SUMMARIZECOLUMNS and CALCULATE as appropriate
Create and manage a shared Fabric connection for the data source

Correct answer: E

Why: Fabric connections centralize connection details and credentials so supported items can reuse governed access. This directly matches the stated requirement.

Option review:

A: Pre-aggregation can reduce repeated scanning of detail rows when consumers consistently query a higher-level grain. This can be valid for ‘Aggregate data’, but it does not directly satisfy the scenario requirement being tested under ‘Create a data connection’.

B: Union or append combines rows from compatible schemas, whereas a join combines columns based on matching keys. This can be valid for ‘Merge or join data’, but it does not directly satisfy the scenario requirement being tested under ‘Create a data connection’.

C: An inner join returns only rows with matching keys on both sides. This can be valid for ‘Merge or join data’, but it does not directly satisfy the scenario requirement being tested under ‘Create a data connection’.

D: DAX queries and functions operate on semantic-model metadata, relationships, and filter context. This can be valid for ‘Select, filter, and aggregate data by using DAX’, but it does not directly satisfy the scenario requirement being tested under ‘Create a data connection’.

E: Fabric connections centralize connection details and credentials so supported items can reuse governed access. This directly matches the stated requirement.

Learning point: Create and manage a shared Fabric connection for the data source

Question 2

A solution architect reviewing Fourth Coffee’s supply-chain lakehouse asks the finance analytics squad to connect a semantic model to an on-premises source that cannot be reached directly from the service. Since the solution should avoid unnecessary custom code, which recommendation is strongest? Existing users should keep their current access.

Flatten the required lookup attributes into the reporting structure
Use an inner join on the matching business or surrogate key
Use a Fabric warehouse
Create a SQL view
Configure the appropriate gateway and connection credentials

Correct answer: E

Why: An on-premises gateway provides the network bridge while the connection stores the credentials and source details. This directly matches the stated requirement.

Option review:

A: Flattening commonly consumed attributes can make read-oriented analytics simpler when update anomalies are controlled upstream. This can be valid for ‘Denormalize data’, but it does not directly satisfy the scenario requirement being tested under ‘Create a data connection’.

B: An inner join returns only rows with matching keys on both sides. This can be valid for ‘Merge or join data’, but it does not directly satisfy the scenario requirement being tested under ‘Create a data connection’.

C: A warehouse is optimized for structured relational analytics and T-SQL based development in Fabric. This can be valid for ‘Choose between different data stores’, but it does not directly satisfy the scenario requirement being tested under ‘Create a data connection’.

D: A view encapsulates a reusable query and presents it as a virtual table. This can be valid for ‘Create views, functions, and stored procedures’, but it does not directly satisfy the scenario requirement being tested under ‘Create a data connection’.

E: An on-premises gateway provides the network bridge while the connection stores the credentials and source details. This directly matches the stated requirement.

Learning point: Configure the appropriate gateway and connection credentials

Question 3

The next sprint for Wide World Importers’s IoT telemetry solution includes a task to reuse governed credentials for a cloud source across Fabric items. The acceptance criteria add that the design must preserve a clear development lifecycle. Which Fabric or Power BI action is appropriate? No unrelated workspace or model permissions should be changed.

Use a left outer join from customers to orders
Add a calculated or derived column in the transformation layer
Use T-SQL with SELECT, JOIN, WHERE, GROUP BY, and aggregate functions as required
Create and manage a shared Fabric connection for the data source
Use the Visual Query Editor to build the query graphically

Correct answer: D

Why: Fabric connections centralize connection details and credentials so supported items can reuse governed access. This directly matches the stated requirement.

Option review:

A: A left outer join retains all rows from the left input and adds matching rows from the right input when available. This can be valid for ‘Merge or join data’, but it does not directly satisfy the scenario requirement being tested under ‘Create a data connection’.

B: A derived column enriches the dataset with business logic while preserving the source fields. This can be valid for ‘Enrich data by adding new columns or tables’, but it does not directly satisfy the scenario requirement being tested under ‘Create a data connection’.

C: SQL is the native relational query language for warehouse and SQL analytics endpoint workloads. This can be valid for ‘Select, filter, and aggregate data by using SQL’, but it does not directly satisfy the scenario requirement being tested under ‘Create a data connection’.

D: Fabric connections centralize connection details and credentials so supported items can reuse governed access. This directly matches the stated requirement.

E: The Visual Query Editor provides a graphical experience for selecting, filtering, joining, and aggregating supported data. This can be valid for ‘Select, filter, and aggregate data by using the Visual query editor’, but it does not directly satisfy the scenario requirement being tested under ‘Create a data connection’.

Learning point: Create and manage a shared Fabric connection for the data source

Question 4

Northwind Traders is troubleshooting a design decision in the finance reporting platform. The desired end state is to connect a semantic model to an on-premises source that cannot be reached directly from the service; the team must avoid granting broader access than required. Which change should the operations data team make? No unrelated workspace or model permissions should be changed.

Use a left outer join from customers to orders
Use a Fabric warehouse
Add a calculated or derived column in the transformation layer
Convert the column to an appropriate date or datetime data type
Configure the appropriate gateway and connection credentials

Correct answer: E

Why: An on-premises gateway provides the network bridge while the connection stores the credentials and source details. This directly matches the stated requirement.

Option review:

B: A warehouse is optimized for structured relational analytics and T-SQL based development in Fabric. This can be valid for ‘Choose between different data stores’, but it does not directly satisfy the scenario requirement being tested under ‘Create a data connection’.

C: A derived column enriches the dataset with business logic while preserving the source fields. This can be valid for ‘Enrich data by adding new columns or tables’, but it does not directly satisfy the scenario requirement being tested under ‘Create a data connection’.

D: Using the correct native type enables valid date comparisons, filtering, and time intelligence. This can be valid for ‘Convert column data types’, but it does not directly satisfy the scenario requirement being tested under ‘Create a data connection’.

E: An on-premises gateway provides the network bridge while the connection stores the credentials and source details. This directly matches the stated requirement.

Learning point: Configure the appropriate gateway and connection credentials

Question 5

For the retail performance dashboard, Trey Research has documented a business requirement to reuse governed credentials for a cloud source across Fabric items. The data governance group must meet it in a way where the change must be easy to audit later. What is the best choice? The team will validate the change first in a nonproduction environment.

Use the Visual Query Editor to build the query graphically
Group the data by month and region and calculate the required summary measures
Use Real-Time hub to browse and connect to real-time data sources
Create and manage a shared Fabric connection for the data source
Build it in the Visual Query Editor

Correct answer: D

Why: Fabric connections centralize connection details and credentials so supported items can reuse governed access. This directly matches the stated requirement.

Option review:

A: The Visual Query Editor provides a graphical experience for selecting, filtering, joining, and aggregating supported data. This can be valid for ‘Select, filter, and aggregate data by using the Visual query editor’, but it does not directly satisfy the scenario requirement being tested under ‘Create a data connection’.

B: Aggregation changes detailed rows into grouped summaries at the required analytical grain. This can be valid for ‘Aggregate data’, but it does not directly satisfy the scenario requirement being tested under ‘Create a data connection’.

C: Real-Time hub is the Fabric experience for discovering, connecting to, and managing real-time event streams and sources. This can be valid for ‘Discover data by using OneLake catalog and Real-Time hub’, but it does not directly satisfy the scenario requirement being tested under ‘Create a data connection’.

D: Fabric connections centralize connection details and credentials so supported items can reuse governed access. This directly matches the stated requirement.

E: The visual editor is intended for graphical query construction while still supporting common relational operations. This can be valid for ‘Select, filter, and aggregate data by using the Visual query editor’, but it does not directly satisfy the scenario requirement being tested under ‘Create a data connection’.

Learning point: Create and manage a shared Fabric connection for the data source

Question 6

A governance review of Alpine Ski House’s marketing semantic model asks for evidence that the solution can connect a semantic model to an on-premises source that cannot be reached directly from the service. Because the implementation should be easy to troubleshoot, which action should be approved? The team will validate the change first in a nonproduction environment.

Apply the row filter as early as practical in the query or transformation
Use an inner join on the matching business or surrogate key
Configure the appropriate gateway and connection credentials
Use the Visual Query Editor to build the query graphically
Create a OneLake shortcut to the existing data

Correct answer: C

Why: An on-premises gateway provides the network bridge while the connection stores the credentials and source details. This directly matches the stated requirement.

Option review:

A: Early filtering reduces the volume of data moved and processed while preserving only the rows required by the workload. This can be valid for ‘Filter data’, but it does not directly satisfy the scenario requirement being tested under ‘Create a data connection’.

C: An on-premises gateway provides the network bridge while the connection stores the credentials and source details. This directly matches the stated requirement.

D: The Visual Query Editor provides a graphical experience for selecting, filtering, joining, and aggregating supported data. This can be valid for ‘Select, filter, and aggregate data by using the Visual query editor’, but it does not directly satisfy the scenario requirement being tested under ‘Create a data connection’.

E: A shortcut provides logical access to supported external or OneLake data without copying the underlying files. This can be valid for ‘Ingest or access data as needed’, but it does not directly satisfy the scenario requirement being tested under ‘Create a data connection’.

Learning point: Configure the appropriate gateway and connection credentials

Question 7

Before expanding the supply-chain lakehouse, the finance analytics squad at Contoso must reuse governed credentials for a cloud source across Fabric items. The rollout plan says that the solution should avoid unnecessary custom code. Which option most directly addresses the requirement? The solution must work with the current Fabric architecture rather than a parallel custom platform.

Create the additional dimension or lookup table during transformation
Filter or remediate rows with missing mandatory keys before loading the curated layer
Use a left outer join from customers to orders
Create and manage a shared Fabric connection for the data source
Model the warehouse with conformed dimensions around fact tables

Correct answer: D

Why: Fabric connections centralize connection details and credentials so supported items can reuse governed access. This directly matches the stated requirement.

Option review:

A: Adding a curated table can enrich the model with reusable business context for downstream joins and analysis. This can be valid for ‘Enrich data by adding new columns or tables’, but it does not directly satisfy the scenario requirement being tested under ‘Create a data connection’.

B: Mandatory-key quality rules should be enforced before downstream joins and aggregations depend on those records. This can be valid for ‘Identify and resolve duplicate data, missing data, or null values’, but it does not directly satisfy the scenario requirement being tested under ‘Create a data connection’.

C: A left outer join retains all rows from the left input and adds matching rows from the right input when available. This can be valid for ‘Merge or join data’, but it does not directly satisfy the scenario requirement being tested under ‘Create a data connection’.

D: Fabric connections centralize connection details and credentials so supported items can reuse governed access. This directly matches the stated requirement.

E: Conformed dimensions and explicit fact grain are core star-schema practices for reliable analytical joins. This can be valid for ‘Implement a star schema for a lakehouse or warehouse’, but it does not directly satisfy the scenario requirement being tested under ‘Create a data connection’.

Learning point: Create and manage a shared Fabric connection for the data source

Question 8

Wingtip Toys is redesigning its IoT telemetry solution. The analytics engineering team must connect a semantic model to an on-premises source that cannot be reached directly from the service. In addition, the design must preserve a clear development lifecycle. Which action is the best fit? The solution must work with the current Fabric architecture rather than a parallel custom platform.

Filter or remediate rows with missing mandatory keys before loading the curated layer
Add a calculated or derived column in the transformation layer
Use a left outer join from customers to orders
Create a fact table at a defined grain with separate dimension tables
Configure the appropriate gateway and connection credentials

Correct answer: E

Why: An on-premises gateway provides the network bridge while the connection stores the credentials and source details. This directly matches the stated requirement.

Option review:

A: Mandatory-key quality rules should be enforced before downstream joins and aggregations depend on those records. This can be valid for ‘Identify and resolve duplicate data, missing data, or null values’, but it does not directly satisfy the scenario requirement being tested under ‘Create a data connection’.

D: A star schema places measurable events in a fact table and descriptive context in dimensions, simplifying analytics and improving model usability. This can be valid for ‘Implement a star schema for a lakehouse or warehouse’, but it does not directly satisfy the scenario requirement being tested under ‘Create a data connection’.

E: An on-premises gateway provides the network bridge while the connection stores the credentials and source details. This directly matches the stated requirement.

Learning point: Configure the appropriate gateway and connection credentials

Question 9

During a design review for Proseware’s finance reporting platform, one requirement is non-negotiable: reuse governed credentials for a cloud source across Fabric items. Because the team must avoid granting broader access than required, what should the operations data team implement? The design decision will be reviewed by both data engineering and BI owners.

Apply the row filter as early as practical in the query or transformation
Create a stored procedure
Use Real-Time hub to browse and connect to real-time data sources
Create and manage a shared Fabric connection for the data source
Denormalize selected attributes into the analytical table

Correct answer: D

Why: Fabric connections centralize connection details and credentials so supported items can reuse governed access. This directly matches the stated requirement.

Option review:

B: A stored procedure packages procedural multi-statement T-SQL operations for repeatable execution. This can be valid for ‘Create views, functions, and stored procedures’, but it does not directly satisfy the scenario requirement being tested under ‘Create a data connection’.

D: Fabric connections centralize connection details and credentials so supported items can reuse governed access. This directly matches the stated requirement.

E: Denormalization trades additional storage and duplication for simpler reads and fewer joins in analytical workloads. This can be valid for ‘Denormalize data’, but it does not directly satisfy the scenario requirement being tested under ‘Create a data connection’.

Learning point: Create and manage a shared Fabric connection for the data source

Question 10

The data governance group at Blue Yonder Airlines is preparing the next release of its retail performance dashboard. They need to connect a semantic model to an on-premises source that cannot be reached directly from the service; the change must be easy to audit later. Which choice most directly satisfies the requirement? The design decision will be reviewed by both data engineering and BI owners.

Configure the appropriate gateway and connection credentials
Use Direct Lake against the OneLake-backed data
Use a Fabric data pipeline Copy activity
Use T-SQL with SELECT, JOIN, WHERE, GROUP BY, and aggregate functions as required
Create a fact table at a defined grain with separate dimension tables

Correct answer: A

Why: An on-premises gateway provides the network bridge while the connection stores the credentials and source details. This directly matches the stated requirement.

Option review:

A: An on-premises gateway provides the network bridge while the connection stores the credentials and source details. This directly matches the stated requirement.

B: Direct Lake lets semantic models query OneLake Delta data with in-memory analytical performance without a traditional import copy. This can be valid for ‘Implement OneLake integration for Eventhouse and semantic models’, but it does not directly satisfy the scenario requirement being tested under ‘Create a data connection’.

C: Copy activity is suited to orchestrated bulk data movement into Fabric with scheduling and monitoring. This can be valid for ‘Ingest or access data as needed’, but it does not directly satisfy the scenario requirement being tested under ‘Create a data connection’.

D: SQL is the native relational query language for warehouse and SQL analytics endpoint workloads. This can be valid for ‘Select, filter, and aggregate data by using SQL’, but it does not directly satisfy the scenario requirement being tested under ‘Create a data connection’.

E: A star schema places measurable events in a fact table and descriptive context in dimensions, simplifying analytics and improving model usability. This can be valid for ‘Implement a star schema for a lakehouse or warehouse’, but it does not directly satisfy the scenario requirement being tested under ‘Create a data connection’.

Learning point: Configure the appropriate gateway and connection credentials

Question 11

A production readiness review at Fabrikam found a gap in the marketing semantic model. The remediation must reuse governed credentials for a cloud source across Fabric items, and the implementation should be easy to troubleshoot. What is the most appropriate action? Existing users should keep their current access.

Create and manage a shared Fabric connection for the data source
Use an Eventhouse with a KQL database
Use Dataflow Gen2 for the ingestion and transformation flow
Apply an explicit predicate that excludes the test rows
Append or union the row sets instead of joining them

Correct answer: A

Why: Fabric connections centralize connection details and credentials so supported items can reuse governed access. This directly matches the stated requirement.

Option review:

A: Fabric connections centralize connection details and credentials so supported items can reuse governed access. This directly matches the stated requirement.

B: Eventhouse and KQL databases are optimized for high-volume real-time, log, and time-series analytics. This can be valid for ‘Choose between different data stores’, but it does not directly satisfy the scenario requirement being tested under ‘Create a data connection’.

C: Dataflow Gen2 provides a Power Query based low-code ingestion and transformation experience. This can be valid for ‘Ingest or access data as needed’, but it does not directly satisfy the scenario requirement being tested under ‘Create a data connection’.

D: A deterministic filter predicate is the direct way to prevent unwanted rows from reaching downstream analysis. This can be valid for ‘Filter data’, but it does not directly satisfy the scenario requirement being tested under ‘Create a data connection’.

E: Union or append combines rows from compatible schemas, whereas a join combines columns based on matching keys. This can be valid for ‘Merge or join data’, but it does not directly satisfy the scenario requirement being tested under ‘Create a data connection’.

Learning point: Create and manage a shared Fabric connection for the data source

Question 12

For a new phase of the supply-chain lakehouse, Litware asks the finance analytics squad to connect a semantic model to an on-premises source that cannot be reached directly from the service. The architecture decision record also states that the solution should avoid unnecessary custom code. Which approach should be selected? Existing users should keep their current access.

Use an Eventhouse with a KQL database
Denormalize selected attributes into the analytical table
Use a DAX query with functions such as SUMMARIZECOLUMNS and CALCULATE as appropriate
Configure the appropriate gateway and connection credentials
Use a Fabric warehouse

Correct answer: D

Why: An on-premises gateway provides the network bridge while the connection stores the credentials and source details. This directly matches the stated requirement.

Option review:

A: Eventhouse and KQL databases are optimized for high-volume real-time, log, and time-series analytics. This can be valid for ‘Choose between different data stores’, but it does not directly satisfy the scenario requirement being tested under ‘Create a data connection’.

B: Denormalization trades additional storage and duplication for simpler reads and fewer joins in analytical workloads. This can be valid for ‘Denormalize data’, but it does not directly satisfy the scenario requirement being tested under ‘Create a data connection’.

C: DAX queries and functions operate on semantic-model metadata, relationships, and filter context. This can be valid for ‘Select, filter, and aggregate data by using DAX’, but it does not directly satisfy the scenario requirement being tested under ‘Create a data connection’.

D: An on-premises gateway provides the network bridge while the connection stores the credentials and source details. This directly matches the stated requirement.

E: A warehouse is optimized for structured relational analytics and T-SQL based development in Fabric. This can be valid for ‘Choose between different data stores’, but it does not directly satisfy the scenario requirement being tested under ‘Create a data connection’.

Learning point: Configure the appropriate gateway and connection credentials

Question 13

Woodgrove Bank is standardizing how the IoT telemetry solution is managed. The immediate goal is to reuse governed credentials for a cloud source across Fabric items. Given that the design must preserve a clear development lifecycle, which option should the analytics engineering team choose? No unrelated workspace or model permissions should be changed.

Use a Fabric warehouse
Flatten the required lookup attributes into the reporting structure
Use Dataflow Gen2 for the ingestion and transformation flow
Create and manage a shared Fabric connection for the data source
Model the warehouse with conformed dimensions around fact tables

Correct answer: D

Why: Fabric connections centralize connection details and credentials so supported items can reuse governed access. This directly matches the stated requirement.

Option review:

A: A warehouse is optimized for structured relational analytics and T-SQL based development in Fabric. This can be valid for ‘Choose between different data stores’, but it does not directly satisfy the scenario requirement being tested under ‘Create a data connection’.

B: Flattening commonly consumed attributes can make read-oriented analytics simpler when update anomalies are controlled upstream. This can be valid for ‘Denormalize data’, but it does not directly satisfy the scenario requirement being tested under ‘Create a data connection’.

D: Fabric connections centralize connection details and credentials so supported items can reuse governed access. This directly matches the stated requirement.

Learning point: Create and manage a shared Fabric connection for the data source

Question 14

An internal audit of Coho Winery’s finance reporting platform identifies this requirement: connect a semantic model to an on-premises source that cannot be reached directly from the service. The operations data team also notes that the team must avoid granting broader access than required. What should they do? No unrelated workspace or model permissions should be changed.

Enable the supported OneLake availability or integration for the Eventhouse data
Configure the appropriate gateway and connection credentials
Use Dataflow Gen2 for the ingestion and transformation flow
Use an inner join on the matching business or surrogate key
Use the OneLake catalog to discover and evaluate available data products

Correct answer: B

Why: An on-premises gateway provides the network bridge while the connection stores the credentials and source details. This directly matches the stated requirement.

Option review:

A: OneLake integration makes supported Eventhouse data discoverable and accessible through OneLake for cross-engine use. This can be valid for ‘Implement OneLake integration for Eventhouse and semantic models’, but it does not directly satisfy the scenario requirement being tested under ‘Create a data connection’.

B: An on-premises gateway provides the network bridge while the connection stores the credentials and source details. This directly matches the stated requirement.

D: An inner join returns only rows with matching keys on both sides. This can be valid for ‘Merge or join data’, but it does not directly satisfy the scenario requirement being tested under ‘Create a data connection’.

E: The OneLake catalog is designed to discover governed Fabric data items across the organization. This can be valid for ‘Discover data by using OneLake catalog and Real-Time hub’, but it does not directly satisfy the scenario requirement being tested under ‘Create a data connection’.

Learning point: Configure the appropriate gateway and connection credentials

Question 15

The retail performance dashboard at Adventure Works is moving from proof of concept to production. Before rollout, the data governance group must reuse governed credentials for a cloud source across Fabric items, while ensuring that the change must be easy to audit later. Which action best meets both needs? The team will validate the change first in a nonproduction environment.

Group the data by month and region and calculate the required summary measures
Use the OneLake catalog to discover and evaluate available data products
Use a Fabric data pipeline Copy activity
Model the warehouse with conformed dimensions around fact tables
Create and manage a shared Fabric connection for the data source

Correct answer: E

Why: Fabric connections centralize connection details and credentials so supported items can reuse governed access. This directly matches the stated requirement.

Option review:

A: Aggregation changes detailed rows into grouped summaries at the required analytical grain. This can be valid for ‘Aggregate data’, but it does not directly satisfy the scenario requirement being tested under ‘Create a data connection’.

B: The OneLake catalog is designed to discover governed Fabric data items across the organization. This can be valid for ‘Discover data by using OneLake catalog and Real-Time hub’, but it does not directly satisfy the scenario requirement being tested under ‘Create a data connection’.

D: Conformed dimensions and explicit fact grain are core star-schema practices for reliable analytical joins. This can be valid for ‘Implement a star schema for a lakehouse or warehouse’, but it does not directly satisfy the scenario requirement being tested under ‘Create a data connection’.

E: Fabric connections centralize connection details and credentials so supported items can reuse governed access. This directly matches the stated requirement.

Learning point: Create and manage a shared Fabric connection for the data source

Question 16

Tailspin Toys has a change request for the customer 360 model: find governed Fabric data items across workspaces by business domain and item metadata. The Fabric center of excellence wants a solution where the implementation should be easy to troubleshoot. Which implementation is most suitable? Existing users should keep their current access.

Use the Visual Query Editor to build the query graphically
Use the OneLake catalog to discover and evaluate available data products
Flatten the required lookup attributes into the reporting structure
Append or union the row sets instead of joining them
Use an inner join on the matching business or surrogate key

Correct answer: B

Why: The OneLake catalog is designed to discover governed Fabric data items across the organization. This directly matches the stated requirement.

Option review:

B: The OneLake catalog is designed to discover governed Fabric data items across the organization. This directly matches the stated requirement.

C: Flattening commonly consumed attributes can make read-oriented analytics simpler when update anomalies are controlled upstream. This can be valid for ‘Denormalize data’, but it does not directly satisfy the scenario requirement being tested under ‘Discover data by using OneLake catalog and Real-Time hub’.

D: Union or append combines rows from compatible schemas, whereas a join combines columns based on matching keys. This can be valid for ‘Merge or join data’, but it does not directly satisfy the scenario requirement being tested under ‘Discover data by using OneLake catalog and Real-Time hub’.

E: An inner join returns only rows with matching keys on both sides. This can be valid for ‘Merge or join data’, but it does not directly satisfy the scenario requirement being tested under ‘Discover data by using OneLake catalog and Real-Time hub’.

Learning point: Use the OneLake catalog to discover and evaluate available data products

Question 17

A solution architect reviewing Fourth Coffee’s sales analytics solution asks the customer insights team to discover available streaming and event sources before building a real-time analytics solution. Since the solution should avoid unnecessary custom code, which recommendation is strongest? Existing users should keep their current access.

Use a Fabric lakehouse
Group the data by month and region and calculate the required summary measures
Use a Power BI semantic model
Handle nulls explicitly with a null-replacement rule such as COALESCE
Use Real-Time hub to browse and connect to real-time data sources

Correct answer: E

Why: Real-Time hub is the Fabric experience for discovering, connecting to, and managing real-time event streams and sources. This directly matches the stated requirement.

Option review:

A: A lakehouse combines OneLake Delta storage with Spark-oriented engineering and a SQL analytics endpoint. This can be valid for ‘Choose between different data stores’, but it does not directly satisfy the scenario requirement being tested under ‘Discover data by using OneLake catalog and Real-Time hub’.

C: A semantic model is the analytical serving layer for governed relationships, measures, and report consumption. This can be valid for ‘Choose between different data stores’, but it does not directly satisfy the scenario requirement being tested under ‘Discover data by using OneLake catalog and Real-Time hub’.

D: Explicit null handling avoids treating missing values as valid zeroes unless the business rule specifically requires that replacement. This can be valid for ‘Identify and resolve duplicate data, missing data, or null values’, but it does not directly satisfy the scenario requirement being tested under ‘Discover data by using OneLake catalog and Real-Time hub’.

E: Real-Time hub is the Fabric experience for discovering, connecting to, and managing real-time event streams and sources. This directly matches the stated requirement.

Learning point: Use Real-Time hub to browse and connect to real-time data sources

Question 18

The next sprint for Wide World Importers’s risk analytics environment includes a task to find governed Fabric data items across workspaces by business domain and item metadata. The acceptance criteria add that the design must preserve a clear development lifecycle. Which Fabric or Power BI action is appropriate? No unrelated workspace or model permissions should be changed.

Use a Fabric warehouse
Use a Power BI semantic model
Use an Eventhouse with a KQL database
Create the additional dimension or lookup table during transformation
Use the OneLake catalog to discover and evaluate available data products

Correct answer: E

Why: The OneLake catalog is designed to discover governed Fabric data items across the organization. This directly matches the stated requirement.

Option review:

B: A semantic model is the analytical serving layer for governed relationships, measures, and report consumption. This can be valid for ‘Choose between different data stores’, but it does not directly satisfy the scenario requirement being tested under ‘Discover data by using OneLake catalog and Real-Time hub’.

C: Eventhouse and KQL databases are optimized for high-volume real-time, log, and time-series analytics. This can be valid for ‘Choose between different data stores’, but it does not directly satisfy the scenario requirement being tested under ‘Discover data by using OneLake catalog and Real-Time hub’.

D: Adding a curated table can enrich the model with reusable business context for downstream joins and analysis. This can be valid for ‘Enrich data by adding new columns or tables’, but it does not directly satisfy the scenario requirement being tested under ‘Discover data by using OneLake catalog and Real-Time hub’.

E: The OneLake catalog is designed to discover governed Fabric data items across the organization. This directly matches the stated requirement.

Learning point: Use the OneLake catalog to discover and evaluate available data products

Question 19

Northwind Traders is troubleshooting a design decision in the service-operations warehouse. The desired end state is to discover available streaming and event sources before building a real-time analytics solution; the team must avoid granting broader access than required. Which change should the BI platform team make? No unrelated workspace or model permissions should be changed.

Precompute the required daily aggregate table or result
Handle nulls explicitly with a null-replacement rule such as COALESCE
Use Real-Time hub to browse and connect to real-time data sources
Create a fact table at a defined grain with separate dimension tables
Append or union the row sets instead of joining them

Correct answer: C

Why: Real-Time hub is the Fabric experience for discovering, connecting to, and managing real-time event streams and sources. This directly matches the stated requirement.

Option review:

B: Explicit null handling avoids treating missing values as valid zeroes unless the business rule specifically requires that replacement. This can be valid for ‘Identify and resolve duplicate data, missing data, or null values’, but it does not directly satisfy the scenario requirement being tested under ‘Discover data by using OneLake catalog and Real-Time hub’.

C: Real-Time hub is the Fabric experience for discovering, connecting to, and managing real-time event streams and sources. This directly matches the stated requirement.

Learning point: Use Real-Time hub to browse and connect to real-time data sources

Question 20

For the executive reporting workspace, Trey Research has documented a business requirement to find governed Fabric data items across workspaces by business domain and item metadata. The security analytics team must meet it in a way where the change must be easy to audit later. What is the best choice? The team will validate the change first in a nonproduction environment.

Create a OneLake shortcut to the existing data
Write the required SQL query against the endpoint
Create a SQL view
Use the OneLake catalog to discover and evaluate available data products
Use an Eventhouse with a KQL database

Correct answer: D

Why: The OneLake catalog is designed to discover governed Fabric data items across the organization. This directly matches the stated requirement.

Option review:

A: A shortcut provides logical access to supported external or OneLake data without copying the underlying files. This can be valid for ‘Ingest or access data as needed’, but it does not directly satisfy the scenario requirement being tested under ‘Discover data by using OneLake catalog and Real-Time hub’.

B: The SQL analytics endpoint supports relational querying with T-SQL for selection, filtering, joins, and aggregation. This can be valid for ‘Select, filter, and aggregate data by using SQL’, but it does not directly satisfy the scenario requirement being tested under ‘Discover data by using OneLake catalog and Real-Time hub’.

C: A view encapsulates a reusable query and presents it as a virtual table. This can be valid for ‘Create views, functions, and stored procedures’, but it does not directly satisfy the scenario requirement being tested under ‘Discover data by using OneLake catalog and Real-Time hub’.

D: The OneLake catalog is designed to discover governed Fabric data items across the organization. This directly matches the stated requirement.

E: Eventhouse and KQL databases are optimized for high-volume real-time, log, and time-series analytics. This can be valid for ‘Choose between different data stores’, but it does not directly satisfy the scenario requirement being tested under ‘Discover data by using OneLake catalog and Real-Time hub’.

Learning point: Use the OneLake catalog to discover and evaluate available data products

Question 21

A governance review of Alpine Ski House’s customer 360 model asks for evidence that the solution can discover available streaming and event sources before building a real-time analytics solution. Because the implementation should be easy to troubleshoot, which action should be approved? The team will validate the change first in a nonproduction environment.

Use Real-Time hub to browse and connect to real-time data sources
Create a stored procedure
Use a Power BI semantic model
Create a SQL view
Add a calculated or derived column in the transformation layer

Correct answer: A

Why: Real-Time hub is the Fabric experience for discovering, connecting to, and managing real-time event streams and sources. This directly matches the stated requirement.

Option review:

A: Real-Time hub is the Fabric experience for discovering, connecting to, and managing real-time event streams and sources. This directly matches the stated requirement.

E: A derived column enriches the dataset with business logic while preserving the source fields. This can be valid for ‘Enrich data by adding new columns or tables’, but it does not directly satisfy the scenario requirement being tested under ‘Discover data by using OneLake catalog and Real-Time hub’.

Learning point: Use Real-Time hub to browse and connect to real-time data sources

Question 22

Before expanding the sales analytics solution, the customer insights team at Contoso must find governed Fabric data items across workspaces by business domain and item metadata. The rollout plan says that the solution should avoid unnecessary custom code. Which option most directly addresses the requirement? The solution must work with the current Fabric architecture rather than a parallel custom platform.

Use Direct Lake against the OneLake-backed data
Group the data by month and region and calculate the required summary measures
Convert the column to an appropriate date or datetime data type
Use the OneLake catalog to discover and evaluate available data products
Model the warehouse with conformed dimensions around fact tables

Correct answer: D

Why: The OneLake catalog is designed to discover governed Fabric data items across the organization. This directly matches the stated requirement.

Option review:

A: Direct Lake lets semantic models query OneLake Delta data with in-memory analytical performance without a traditional import copy. This can be valid for ‘Implement OneLake integration for Eventhouse and semantic models’, but it does not directly satisfy the scenario requirement being tested under ‘Discover data by using OneLake catalog and Real-Time hub’.

C: Using the correct native type enables valid date comparisons, filtering, and time intelligence. This can be valid for ‘Convert column data types’, but it does not directly satisfy the scenario requirement being tested under ‘Discover data by using OneLake catalog and Real-Time hub’.

D: The OneLake catalog is designed to discover governed Fabric data items across the organization. This directly matches the stated requirement.

Learning point: Use the OneLake catalog to discover and evaluate available data products

Question 23

Wingtip Toys is redesigning its risk analytics environment. The retail insights team must discover available streaming and event sources before building a real-time analytics solution. In addition, the design must preserve a clear development lifecycle. Which action is the best fit? The solution must work with the current Fabric architecture rather than a parallel custom platform.

Cast the column to the appropriate numeric type before aggregation
Use Real-Time hub to browse and connect to real-time data sources
Use T-SQL with SELECT, JOIN, WHERE, GROUP BY, and aggregate functions as required
Use the Visual Query Editor to build the query graphically
Flatten the required lookup attributes into the reporting structure

Correct answer: B

Why: Real-Time hub is the Fabric experience for discovering, connecting to, and managing real-time event streams and sources. This directly matches the stated requirement.

Option review:

A: Numeric calculations require a compatible numeric type and should avoid relying on implicit conversion. This can be valid for ‘Convert column data types’, but it does not directly satisfy the scenario requirement being tested under ‘Discover data by using OneLake catalog and Real-Time hub’.

B: Real-Time hub is the Fabric experience for discovering, connecting to, and managing real-time event streams and sources. This directly matches the stated requirement.

E: Flattening commonly consumed attributes can make read-oriented analytics simpler when update anomalies are controlled upstream. This can be valid for ‘Denormalize data’, but it does not directly satisfy the scenario requirement being tested under ‘Discover data by using OneLake catalog and Real-Time hub’.

Learning point: Use Real-Time hub to browse and connect to real-time data sources

Question 24

During a design review for Proseware’s service-operations warehouse, one requirement is non-negotiable: find governed Fabric data items across workspaces by business domain and item metadata. Because the team must avoid granting broader access than required, what should the BI platform team implement? The design decision will be reviewed by both data engineering and BI owners.

Use the OneLake catalog to discover and evaluate available data products
Use a Fabric warehouse
Identify the duplicate keys and deduplicate according to the authoritative business rule
Convert the column to an appropriate date or datetime data type
Create a DAX measure using the required aggregation and filter context logic

Correct answer: A

Why: The OneLake catalog is designed to discover governed Fabric data items across the organization. This directly matches the stated requirement.

Option review:

A: The OneLake catalog is designed to discover governed Fabric data items across the organization. This directly matches the stated requirement.

C: Deduplication must preserve the correct authoritative row while eliminating records that would inflate analytical results. This can be valid for ‘Identify and resolve duplicate data, missing data, or null values’, but it does not directly satisfy the scenario requirement being tested under ‘Discover data by using OneLake catalog and Real-Time hub’.

E: DAX measures evaluate in semantic-model filter context and are the native way to define reusable analytical calculations. This can be valid for ‘Select, filter, and aggregate data by using DAX’, but it does not directly satisfy the scenario requirement being tested under ‘Discover data by using OneLake catalog and Real-Time hub’.

Learning point: Use the OneLake catalog to discover and evaluate available data products

Question 25

The security analytics team at Blue Yonder Airlines is preparing the next release of its executive reporting workspace. They need to discover available streaming and event sources before building a real-time analytics solution; the change must be easy to audit later. Which choice most directly satisfies the requirement? The design decision will be reviewed by both data engineering and BI owners.

Identify the duplicate keys and deduplicate according to the authoritative business rule
Use Dataflow Gen2 for the ingestion and transformation flow
Create the additional dimension or lookup table during transformation
Write the required SQL query against the endpoint
Use Real-Time hub to browse and connect to real-time data sources

Correct answer: E

Why: Real-Time hub is the Fabric experience for discovering, connecting to, and managing real-time event streams and sources. This directly matches the stated requirement.

Option review:

A: Deduplication must preserve the correct authoritative row while eliminating records that would inflate analytical results. This can be valid for ‘Identify and resolve duplicate data, missing data, or null values’, but it does not directly satisfy the scenario requirement being tested under ‘Discover data by using OneLake catalog and Real-Time hub’.

B: Dataflow Gen2 provides a Power Query based low-code ingestion and transformation experience. This can be valid for ‘Ingest or access data as needed’, but it does not directly satisfy the scenario requirement being tested under ‘Discover data by using OneLake catalog and Real-Time hub’.

C: Adding a curated table can enrich the model with reusable business context for downstream joins and analysis. This can be valid for ‘Enrich data by adding new columns or tables’, but it does not directly satisfy the scenario requirement being tested under ‘Discover data by using OneLake catalog and Real-Time hub’.

D: The SQL analytics endpoint supports relational querying with T-SQL for selection, filtering, joins, and aggregation. This can be valid for ‘Select, filter, and aggregate data by using SQL’, but it does not directly satisfy the scenario requirement being tested under ‘Discover data by using OneLake catalog and Real-Time hub’.

E: Real-Time hub is the Fabric experience for discovering, connecting to, and managing real-time event streams and sources. This directly matches the stated requirement.

Learning point: Use Real-Time hub to browse and connect to real-time data sources

Question 26

A production readiness review at Fabrikam found a gap in the customer 360 model. The remediation must find governed Fabric data items across workspaces by business domain and item metadata, and the implementation should be easy to troubleshoot. What is the most appropriate action? Existing users should keep their current access.

Use a Power BI semantic model
Filter or remediate rows with missing mandatory keys before loading the curated layer
Use the Visual Query Editor to build the query graphically
Use the OneLake catalog to discover and evaluate available data products
Use a Fabric warehouse

Correct answer: D

Why: The OneLake catalog is designed to discover governed Fabric data items across the organization. This directly matches the stated requirement.

Option review:

A: A semantic model is the analytical serving layer for governed relationships, measures, and report consumption. This can be valid for ‘Choose between different data stores’, but it does not directly satisfy the scenario requirement being tested under ‘Discover data by using OneLake catalog and Real-Time hub’.

C: The Visual Query Editor provides a graphical experience for selecting, filtering, joining, and aggregating supported data. This can be valid for ‘Select, filter, and aggregate data by using the Visual query editor’, but it does not directly satisfy the scenario requirement being tested under ‘Discover data by using OneLake catalog and Real-Time hub’.

D: The OneLake catalog is designed to discover governed Fabric data items across the organization. This directly matches the stated requirement.

Learning point: Use the OneLake catalog to discover and evaluate available data products

Question 27

For a new phase of the sales analytics solution, Litware asks the customer insights team to discover available streaming and event sources before building a real-time analytics solution. The architecture decision record also states that the solution should avoid unnecessary custom code. Which approach should be selected? Existing users should keep their current access.

Use Real-Time hub to browse and connect to real-time data sources
Apply the row filter as early as practical in the query or transformation
Create a DAX measure using the required aggregation and filter context logic
Create a SQL view
Add a calculated or derived column in the transformation layer

Correct answer: A

Why: Real-Time hub is the Fabric experience for discovering, connecting to, and managing real-time event streams and sources. This directly matches the stated requirement.

Option review:

A: Real-Time hub is the Fabric experience for discovering, connecting to, and managing real-time event streams and sources. This directly matches the stated requirement.

B: Early filtering reduces the volume of data moved and processed while preserving only the rows required by the workload. This can be valid for ‘Filter data’, but it does not directly satisfy the scenario requirement being tested under ‘Discover data by using OneLake catalog and Real-Time hub’.

C: DAX measures evaluate in semantic-model filter context and are the native way to define reusable analytical calculations. This can be valid for ‘Select, filter, and aggregate data by using DAX’, but it does not directly satisfy the scenario requirement being tested under ‘Discover data by using OneLake catalog and Real-Time hub’.

Learning point: Use Real-Time hub to browse and connect to real-time data sources

Question 28

Woodgrove Bank is standardizing how the risk analytics environment is managed. The immediate goal is to find governed Fabric data items across workspaces by business domain and item metadata. Given that the design must preserve a clear development lifecycle, which option should the retail insights team choose? No unrelated workspace or model permissions should be changed.

Use a Fabric data pipeline Copy activity
Create a fact table at a defined grain with separate dimension tables
Create a SQL function
Group the data by month and region and calculate the required summary measures
Use the OneLake catalog to discover and evaluate available data products

Correct answer: E

Why: The OneLake catalog is designed to discover governed Fabric data items across the organization. This directly matches the stated requirement.

Option review:

A: Copy activity is suited to orchestrated bulk data movement into Fabric with scheduling and monitoring. This can be valid for ‘Ingest or access data as needed’, but it does not directly satisfy the scenario requirement being tested under ‘Discover data by using OneLake catalog and Real-Time hub’.

B: A star schema places measurable events in a fact table and descriptive context in dimensions, simplifying analytics and improving model usability. This can be valid for ‘Implement a star schema for a lakehouse or warehouse’, but it does not directly satisfy the scenario requirement being tested under ‘Discover data by using OneLake catalog and Real-Time hub’.

C: A function is appropriate for reusable parameterized logic that returns a value or table expression. This can be valid for ‘Create views, functions, and stored procedures’, but it does not directly satisfy the scenario requirement being tested under ‘Discover data by using OneLake catalog and Real-Time hub’.

D: Aggregation changes detailed rows into grouped summaries at the required analytical grain. This can be valid for ‘Aggregate data’, but it does not directly satisfy the scenario requirement being tested under ‘Discover data by using OneLake catalog and Real-Time hub’.

E: The OneLake catalog is designed to discover governed Fabric data items across the organization. This directly matches the stated requirement.

Learning point: Use the OneLake catalog to discover and evaluate available data products

Question 29

An internal audit of Coho Winery’s service-operations warehouse identifies this requirement: discover available streaming and event sources before building a real-time analytics solution. The BI platform team also notes that the team must avoid granting broader access than required. What should they do? No unrelated workspace or model permissions should be changed.

Use a left outer join from customers to orders
Build it in the Visual Query Editor
Use Real-Time hub to browse and connect to real-time data sources
Apply an explicit predicate that excludes the test rows
Enable the supported OneLake availability or integration for the Eventhouse data

Correct answer: C

Why: Real-Time hub is the Fabric experience for discovering, connecting to, and managing real-time event streams and sources. This directly matches the stated requirement.

Option review:

B: The visual editor is intended for graphical query construction while still supporting common relational operations. This can be valid for ‘Select, filter, and aggregate data by using the Visual query editor’, but it does not directly satisfy the scenario requirement being tested under ‘Discover data by using OneLake catalog and Real-Time hub’.

C: Real-Time hub is the Fabric experience for discovering, connecting to, and managing real-time event streams and sources. This directly matches the stated requirement.

E: OneLake integration makes supported Eventhouse data discoverable and accessible through OneLake for cross-engine use. This can be valid for ‘Implement OneLake integration for Eventhouse and semantic models’, but it does not directly satisfy the scenario requirement being tested under ‘Discover data by using OneLake catalog and Real-Time hub’.

Learning point: Use Real-Time hub to browse and connect to real-time data sources

Question 30

The executive reporting workspace at Adventure Works is moving from proof of concept to production. Before rollout, the security analytics team must find governed Fabric data items across workspaces by business domain and item metadata, while ensuring that the change must be easy to audit later. Which action best meets both needs? The team will validate the change first in a nonproduction environment.

Use the OneLake catalog to discover and evaluate available data products
Use a Fabric lakehouse
Use KQL operators such as where, summarize, and bin against the KQL database
Build it in the Visual Query Editor
Write the required SQL query against the endpoint

Correct answer: A

Why: The OneLake catalog is designed to discover governed Fabric data items across the organization. This directly matches the stated requirement.

Option review:

A: The OneLake catalog is designed to discover governed Fabric data items across the organization. This directly matches the stated requirement.

B: A lakehouse combines OneLake Delta storage with Spark-oriented engineering and a SQL analytics endpoint. This can be valid for ‘Choose between different data stores’, but it does not directly satisfy the scenario requirement being tested under ‘Discover data by using OneLake catalog and Real-Time hub’.

C: KQL is optimized for filtering and aggregating log, telemetry, and time-series data in Eventhouse. This can be valid for ‘Select, filter, and aggregate data by using KQL’, but it does not directly satisfy the scenario requirement being tested under ‘Discover data by using OneLake catalog and Real-Time hub’.

D: The visual editor is intended for graphical query construction while still supporting common relational operations. This can be valid for ‘Select, filter, and aggregate data by using the Visual query editor’, but it does not directly satisfy the scenario requirement being tested under ‘Discover data by using OneLake catalog and Real-Time hub’.

E: The SQL analytics endpoint supports relational querying with T-SQL for selection, filtering, joins, and aggregation. This can be valid for ‘Select, filter, and aggregate data by using SQL’, but it does not directly satisfy the scenario requirement being tested under ‘Discover data by using OneLake catalog and Real-Time hub’.

Learning point: Use the OneLake catalog to discover and evaluate available data products

Question 31

Tailspin Toys has a change request for the marketing semantic model: use data already stored in another supported location without duplicating the underlying files into the lakehouse. The enterprise reporting group wants a solution where the implementation should be easy to troubleshoot. Which implementation is most suitable? Existing users should keep their current access.

Use a Fabric warehouse
Create a OneLake shortcut to the existing data
Use Direct Lake against the OneLake-backed data
Create a fact table at a defined grain with separate dimension tables
Cast the column to the appropriate numeric type before aggregation

Correct answer: B

Why: A shortcut provides logical access to supported external or OneLake data without copying the underlying files. This directly matches the stated requirement.

Option review:

B: A shortcut provides logical access to supported external or OneLake data without copying the underlying files. This directly matches the stated requirement.

C: Direct Lake lets semantic models query OneLake Delta data with in-memory analytical performance without a traditional import copy. This can be valid for ‘Implement OneLake integration for Eventhouse and semantic models’, but it does not directly satisfy the scenario requirement being tested under ‘Ingest or access data as needed’.

E: Numeric calculations require a compatible numeric type and should avoid relying on implicit conversion. This can be valid for ‘Convert column data types’, but it does not directly satisfy the scenario requirement being tested under ‘Ingest or access data as needed’.

Learning point: Create a OneLake shortcut to the existing data

Question 32

A solution architect reviewing Fourth Coffee’s supply-chain lakehouse asks the finance analytics squad to perform scheduled bulk movement from a source system into Fabric with orchestration and monitoring. Since the solution should avoid unnecessary custom code, which recommendation is strongest? Existing users should keep their current access.

Use a Fabric data pipeline Copy activity
Use a DAX query with functions such as SUMMARIZECOLUMNS and CALCULATE as appropriate
Create a stored procedure
Use Real-Time hub to browse and connect to real-time data sources
Use T-SQL with SELECT, JOIN, WHERE, GROUP BY, and aggregate functions as required

Correct answer: A

Why: Copy activity is suited to orchestrated bulk data movement into Fabric with scheduling and monitoring. This directly matches the stated requirement.

Option review:

A: Copy activity is suited to orchestrated bulk data movement into Fabric with scheduling and monitoring. This directly matches the stated requirement.

B: DAX queries and functions operate on semantic-model metadata, relationships, and filter context. This can be valid for ‘Select, filter, and aggregate data by using DAX’, but it does not directly satisfy the scenario requirement being tested under ‘Ingest or access data as needed’.

C: A stored procedure packages procedural multi-statement T-SQL operations for repeatable execution. This can be valid for ‘Create views, functions, and stored procedures’, but it does not directly satisfy the scenario requirement being tested under ‘Ingest or access data as needed’.

D: Real-Time hub is the Fabric experience for discovering, connecting to, and managing real-time event streams and sources. This can be valid for ‘Discover data by using OneLake catalog and Real-Time hub’, but it does not directly satisfy the scenario requirement being tested under ‘Ingest or access data as needed’.

E: SQL is the native relational query language for warehouse and SQL analytics endpoint workloads. This can be valid for ‘Select, filter, and aggregate data by using SQL’, but it does not directly satisfy the scenario requirement being tested under ‘Ingest or access data as needed’.

Learning point: Use a Fabric data pipeline Copy activity

Question 33

The next sprint for Wide World Importers’s IoT telemetry solution includes a task to apply low-code shaping while ingesting operational source data into Fabric. The acceptance criteria add that the design must preserve a clear development lifecycle. Which Fabric or Power BI action is appropriate? Existing users should keep their current access.

Use Dataflow Gen2 for the ingestion and transformation flow
Build it in the Visual Query Editor
Filter or remediate rows with missing mandatory keys before loading the curated layer
Group the data by month and region and calculate the required summary measures
Use T-SQL with SELECT, JOIN, WHERE, GROUP BY, and aggregate functions as required

Correct answer: A

Why: Dataflow Gen2 provides a Power Query based low-code ingestion and transformation experience. This directly matches the stated requirement.

Option review:

A: Dataflow Gen2 provides a Power Query based low-code ingestion and transformation experience. This directly matches the stated requirement.

C: Mandatory-key quality rules should be enforced before downstream joins and aggregations depend on those records. This can be valid for ‘Identify and resolve duplicate data, missing data, or null values’, but it does not directly satisfy the scenario requirement being tested under ‘Ingest or access data as needed’.

Learning point: Use Dataflow Gen2 for the ingestion and transformation flow

Question 34

Northwind Traders is troubleshooting a design decision in the finance reporting platform. The desired end state is to use data already stored in another supported location without duplicating the underlying files into the lakehouse; the team must avoid granting broader access than required. Which change should the operations data team make? No unrelated workspace or model permissions should be changed.

Use a left outer join from customers to orders
Denormalize selected attributes into the analytical table
Write the required SQL query against the endpoint
Use Real-Time hub to browse and connect to real-time data sources
Create a OneLake shortcut to the existing data

Correct answer: E

Why: A shortcut provides logical access to supported external or OneLake data without copying the underlying files. This directly matches the stated requirement.

Option review:

C: The SQL analytics endpoint supports relational querying with T-SQL for selection, filtering, joins, and aggregation. This can be valid for ‘Select, filter, and aggregate data by using SQL’, but it does not directly satisfy the scenario requirement being tested under ‘Ingest or access data as needed’.

E: A shortcut provides logical access to supported external or OneLake data without copying the underlying files. This directly matches the stated requirement.

Learning point: Create a OneLake shortcut to the existing data

Question 35

For the retail performance dashboard, Trey Research has documented a business requirement to perform scheduled bulk movement from a source system into Fabric with orchestration and monitoring. The data governance group must meet it in a way where the change must be easy to audit later. What is the best choice? No unrelated workspace or model permissions should be changed.

Build it in the Visual Query Editor
Create and manage a shared Fabric connection for the data source
Use a Fabric data pipeline Copy activity
Cast the column to the appropriate numeric type before aggregation
Create the additional dimension or lookup table during transformation

Correct answer: C

Why: Copy activity is suited to orchestrated bulk data movement into Fabric with scheduling and monitoring. This directly matches the stated requirement.

Option review:

A: The visual editor is intended for graphical query construction while still supporting common relational operations. This can be valid for ‘Select, filter, and aggregate data by using the Visual query editor’, but it does not directly satisfy the scenario requirement being tested under ‘Ingest or access data as needed’.

B: Fabric connections centralize connection details and credentials so supported items can reuse governed access. This can be valid for ‘Create a data connection’, but it does not directly satisfy the scenario requirement being tested under ‘Ingest or access data as needed’.

C: Copy activity is suited to orchestrated bulk data movement into Fabric with scheduling and monitoring. This directly matches the stated requirement.

D: Numeric calculations require a compatible numeric type and should avoid relying on implicit conversion. This can be valid for ‘Convert column data types’, but it does not directly satisfy the scenario requirement being tested under ‘Ingest or access data as needed’.

E: Adding a curated table can enrich the model with reusable business context for downstream joins and analysis. This can be valid for ‘Enrich data by adding new columns or tables’, but it does not directly satisfy the scenario requirement being tested under ‘Ingest or access data as needed’.

Learning point: Use a Fabric data pipeline Copy activity

Question 36

A governance review of Alpine Ski House’s marketing semantic model asks for evidence that the solution can apply low-code shaping while ingesting operational source data into Fabric. Because the implementation should be easy to troubleshoot, which action should be approved? No unrelated workspace or model permissions should be changed.

Filter or remediate rows with missing mandatory keys before loading the curated layer
Use Dataflow Gen2 for the ingestion and transformation flow
Use an inner join on the matching business or surrogate key
Build it in the Visual Query Editor
Use T-SQL with SELECT, JOIN, WHERE, GROUP BY, and aggregate functions as required

Correct answer: B

Why: Dataflow Gen2 provides a Power Query based low-code ingestion and transformation experience. This directly matches the stated requirement.

Option review:

B: Dataflow Gen2 provides a Power Query based low-code ingestion and transformation experience. This directly matches the stated requirement.

Learning point: Use Dataflow Gen2 for the ingestion and transformation flow

Question 37

Before expanding the supply-chain lakehouse, the finance analytics squad at Contoso must use data already stored in another supported location without duplicating the underlying files into the lakehouse. The rollout plan says that the solution should avoid unnecessary custom code. Which option most directly addresses the requirement? The team will validate the change first in a nonproduction environment.

Use the OneLake catalog to discover and evaluate available data products
Create a OneLake shortcut to the existing data
Use an Eventhouse with a KQL database
Denormalize selected attributes into the analytical table
Use Direct Lake against the OneLake-backed data

Correct answer: B

Why: A shortcut provides logical access to supported external or OneLake data without copying the underlying files. This directly matches the stated requirement.

Option review:

A: The OneLake catalog is designed to discover governed Fabric data items across the organization. This can be valid for ‘Discover data by using OneLake catalog and Real-Time hub’, but it does not directly satisfy the scenario requirement being tested under ‘Ingest or access data as needed’.

B: A shortcut provides logical access to supported external or OneLake data without copying the underlying files. This directly matches the stated requirement.

D: Denormalization trades additional storage and duplication for simpler reads and fewer joins in analytical workloads. This can be valid for ‘Denormalize data’, but it does not directly satisfy the scenario requirement being tested under ‘Ingest or access data as needed’.

E: Direct Lake lets semantic models query OneLake Delta data with in-memory analytical performance without a traditional import copy. This can be valid for ‘Implement OneLake integration for Eventhouse and semantic models’, but it does not directly satisfy the scenario requirement being tested under ‘Ingest or access data as needed’.

Learning point: Create a OneLake shortcut to the existing data

Question 38

Wingtip Toys is redesigning its IoT telemetry solution. The analytics engineering team must perform scheduled bulk movement from a source system into Fabric with orchestration and monitoring. In addition, the design must preserve a clear development lifecycle. Which action is the best fit? The team will validate the change first in a nonproduction environment.

Write the required SQL query against the endpoint
Use a Fabric data pipeline Copy activity
Build it in the Visual Query Editor
Use KQL project and where operators
Use a Fabric lakehouse

Correct answer: B

Why: Copy activity is suited to orchestrated bulk data movement into Fabric with scheduling and monitoring. This directly matches the stated requirement.

Option review:

A: The SQL analytics endpoint supports relational querying with T-SQL for selection, filtering, joins, and aggregation. This can be valid for ‘Select, filter, and aggregate data by using SQL’, but it does not directly satisfy the scenario requirement being tested under ‘Ingest or access data as needed’.

B: Copy activity is suited to orchestrated bulk data movement into Fabric with scheduling and monitoring. This directly matches the stated requirement.

C: The visual editor is intended for graphical query construction while still supporting common relational operations. This can be valid for ‘Select, filter, and aggregate data by using the Visual query editor’, but it does not directly satisfy the scenario requirement being tested under ‘Ingest or access data as needed’.

D: KQL provides concise operators for selecting columns and filtering event data. This can be valid for ‘Select, filter, and aggregate data by using KQL’, but it does not directly satisfy the scenario requirement being tested under ‘Ingest or access data as needed’.

E: A lakehouse combines OneLake Delta storage with Spark-oriented engineering and a SQL analytics endpoint. This can be valid for ‘Choose between different data stores’, but it does not directly satisfy the scenario requirement being tested under ‘Ingest or access data as needed’.

Learning point: Use a Fabric data pipeline Copy activity

Question 39

During a design review for Proseware’s finance reporting platform, one requirement is non-negotiable: apply low-code shaping while ingesting operational source data into Fabric. Because the team must avoid granting broader access than required, what should the operations data team implement? The team will validate the change first in a nonproduction environment.

Use the OneLake catalog to discover and evaluate available data products
Create a SQL function
Use Dataflow Gen2 for the ingestion and transformation flow
Use an Eventhouse with a KQL database
Use the Visual Query Editor to build the query graphically

Correct answer: C

Why: Dataflow Gen2 provides a Power Query based low-code ingestion and transformation experience. This directly matches the stated requirement.

Option review:

B: A function is appropriate for reusable parameterized logic that returns a value or table expression. This can be valid for ‘Create views, functions, and stored procedures’, but it does not directly satisfy the scenario requirement being tested under ‘Ingest or access data as needed’.

C: Dataflow Gen2 provides a Power Query based low-code ingestion and transformation experience. This directly matches the stated requirement.

D: Eventhouse and KQL databases are optimized for high-volume real-time, log, and time-series analytics. This can be valid for ‘Choose between different data stores’, but it does not directly satisfy the scenario requirement being tested under ‘Ingest or access data as needed’.

Learning point: Use Dataflow Gen2 for the ingestion and transformation flow

Question 40

The data governance group at Blue Yonder Airlines is preparing the next release of its retail performance dashboard. They need to use data already stored in another supported location without duplicating the underlying files into the lakehouse; the change must be easy to audit later. Which choice most directly satisfies the requirement? The solution must work with the current Fabric architecture rather than a parallel custom platform.

Build it in the Visual Query Editor
Use KQL operators such as where, summarize, and bin against the KQL database
Create a OneLake shortcut to the existing data
Create a stored procedure
Write the required SQL query against the endpoint

Correct answer: C

Why: A shortcut provides logical access to supported external or OneLake data without copying the underlying files. This directly matches the stated requirement.

Option review:

B: KQL is optimized for filtering and aggregating log, telemetry, and time-series data in Eventhouse. This can be valid for ‘Select, filter, and aggregate data by using KQL’, but it does not directly satisfy the scenario requirement being tested under ‘Ingest or access data as needed’.

C: A shortcut provides logical access to supported external or OneLake data without copying the underlying files. This directly matches the stated requirement.

D: A stored procedure packages procedural multi-statement T-SQL operations for repeatable execution. This can be valid for ‘Create views, functions, and stored procedures’, but it does not directly satisfy the scenario requirement being tested under ‘Ingest or access data as needed’.

Learning point: Create a OneLake shortcut to the existing data

Question 41

A production readiness review at Fabrikam found a gap in the marketing semantic model. The remediation must perform scheduled bulk movement from a source system into Fabric with orchestration and monitoring, and the implementation should be easy to troubleshoot. What is the most appropriate action? The solution must work with the current Fabric architecture rather than a parallel custom platform.

Use the OneLake catalog to discover and evaluate available data products
Configure the appropriate gateway and connection credentials
Use a Fabric data pipeline Copy activity
Model the warehouse with conformed dimensions around fact tables
Use a Fabric warehouse

Correct answer: C

Why: Copy activity is suited to orchestrated bulk data movement into Fabric with scheduling and monitoring. This directly matches the stated requirement.

Option review:

B: An on-premises gateway provides the network bridge while the connection stores the credentials and source details. This can be valid for ‘Create a data connection’, but it does not directly satisfy the scenario requirement being tested under ‘Ingest or access data as needed’.

C: Copy activity is suited to orchestrated bulk data movement into Fabric with scheduling and monitoring. This directly matches the stated requirement.

Learning point: Use a Fabric data pipeline Copy activity

Question 42

For a new phase of the supply-chain lakehouse, Litware asks the finance analytics squad to apply low-code shaping while ingesting operational source data into Fabric. The architecture decision record also states that the solution should avoid unnecessary custom code. Which approach should be selected? The solution must work with the current Fabric architecture rather than a parallel custom platform.

Apply an explicit predicate that excludes the test rows
Write the required SQL query against the endpoint
Use Dataflow Gen2 for the ingestion and transformation flow
Use Direct Lake against the OneLake-backed data
Handle nulls explicitly with a null-replacement rule such as COALESCE

Correct answer: C

Why: Dataflow Gen2 provides a Power Query based low-code ingestion and transformation experience. This directly matches the stated requirement.

Option review:

A: A deterministic filter predicate is the direct way to prevent unwanted rows from reaching downstream analysis. This can be valid for ‘Filter data’, but it does not directly satisfy the scenario requirement being tested under ‘Ingest or access data as needed’.

C: Dataflow Gen2 provides a Power Query based low-code ingestion and transformation experience. This directly matches the stated requirement.

D: Direct Lake lets semantic models query OneLake Delta data with in-memory analytical performance without a traditional import copy. This can be valid for ‘Implement OneLake integration for Eventhouse and semantic models’, but it does not directly satisfy the scenario requirement being tested under ‘Ingest or access data as needed’.

E: Explicit null handling avoids treating missing values as valid zeroes unless the business rule specifically requires that replacement. This can be valid for ‘Identify and resolve duplicate data, missing data, or null values’, but it does not directly satisfy the scenario requirement being tested under ‘Ingest or access data as needed’.

Learning point: Use Dataflow Gen2 for the ingestion and transformation flow

Question 43

Woodgrove Bank is standardizing how the IoT telemetry solution is managed. The immediate goal is to use data already stored in another supported location without duplicating the underlying files into the lakehouse. Given that the design must preserve a clear development lifecycle, which option should the analytics engineering team choose? The design decision will be reviewed by both data engineering and BI owners.

Write the required SQL query against the endpoint
Configure the appropriate gateway and connection credentials
Use a Power BI semantic model
Identify the duplicate keys and deduplicate according to the authoritative business rule
Create a OneLake shortcut to the existing data

Correct answer: E

Why: A shortcut provides logical access to supported external or OneLake data without copying the underlying files. This directly matches the stated requirement.

Option review:

D: Deduplication must preserve the correct authoritative row while eliminating records that would inflate analytical results. This can be valid for ‘Identify and resolve duplicate data, missing data, or null values’, but it does not directly satisfy the scenario requirement being tested under ‘Ingest or access data as needed’.

E: A shortcut provides logical access to supported external or OneLake data without copying the underlying files. This directly matches the stated requirement.

Learning point: Create a OneLake shortcut to the existing data

Question 44

An internal audit of Coho Winery’s finance reporting platform identifies this requirement: perform scheduled bulk movement from a source system into Fabric with orchestration and monitoring. The operations data team also notes that the team must avoid granting broader access than required. What should they do? The design decision will be reviewed by both data engineering and BI owners.

Use a Power BI semantic model
Use a Fabric data pipeline Copy activity
Use Direct Lake against the OneLake-backed data
Append or union the row sets instead of joining them
Use a Fabric warehouse

Correct answer: B

Why: Copy activity is suited to orchestrated bulk data movement into Fabric with scheduling and monitoring. This directly matches the stated requirement.

Option review:

B: Copy activity is suited to orchestrated bulk data movement into Fabric with scheduling and monitoring. This directly matches the stated requirement.

Learning point: Use a Fabric data pipeline Copy activity

Question 45

The retail performance dashboard at Adventure Works is moving from proof of concept to production. Before rollout, the data governance group must apply low-code shaping while ingesting operational source data into Fabric, while ensuring that the change must be easy to audit later. Which action best meets both needs? The design decision will be reviewed by both data engineering and BI owners.

Use Dataflow Gen2 for the ingestion and transformation flow
Enable the supported OneLake availability or integration for the Eventhouse data
Create a fact table at a defined grain with separate dimension tables
Write the required SQL query against the endpoint
Identify the duplicate keys and deduplicate according to the authoritative business rule

Correct answer: A

Why: Dataflow Gen2 provides a Power Query based low-code ingestion and transformation experience. This directly matches the stated requirement.

Option review:

A: Dataflow Gen2 provides a Power Query based low-code ingestion and transformation experience. This directly matches the stated requirement.

B: OneLake integration makes supported Eventhouse data discoverable and accessible through OneLake for cross-engine use. This can be valid for ‘Implement OneLake integration for Eventhouse and semantic models’, but it does not directly satisfy the scenario requirement being tested under ‘Ingest or access data as needed’.

C: A star schema places measurable events in a fact table and descriptive context in dimensions, simplifying analytics and improving model usability. This can be valid for ‘Implement a star schema for a lakehouse or warehouse’, but it does not directly satisfy the scenario requirement being tested under ‘Ingest or access data as needed’.

E: Deduplication must preserve the correct authoritative row while eliminating records that would inflate analytical results. This can be valid for ‘Identify and resolve duplicate data, missing data, or null values’, but it does not directly satisfy the scenario requirement being tested under ‘Ingest or access data as needed’.

Learning point: Use Dataflow Gen2 for the ingestion and transformation flow

Question 46

Tailspin Toys has a change request for the customer 360 model: support Spark engineering over Delta files while also exposing a SQL analytics endpoint for analytics. The Fabric center of excellence wants a solution where the implementation should be easy to troubleshoot. Which implementation is most suitable? Existing users should keep their current access.

Use a Fabric lakehouse
Enable the supported OneLake availability or integration for the Eventhouse data
Handle nulls explicitly with a null-replacement rule such as COALESCE
Use Dataflow Gen2 for the ingestion and transformation flow
Use the OneLake catalog to discover and evaluate available data products

Correct answer: A

Why: A lakehouse combines OneLake Delta storage with Spark-oriented engineering and a SQL analytics endpoint. This directly matches the stated requirement.

Option review:

A: A lakehouse combines OneLake Delta storage with Spark-oriented engineering and a SQL analytics endpoint. This directly matches the stated requirement.

C: Explicit null handling avoids treating missing values as valid zeroes unless the business rule specifically requires that replacement. This can be valid for ‘Identify and resolve duplicate data, missing data, or null values’, but it does not directly satisfy the scenario requirement being tested under ‘Choose between different data stores’.

D: Dataflow Gen2 provides a Power Query based low-code ingestion and transformation experience. This can be valid for ‘Ingest or access data as needed’, but it does not directly satisfy the scenario requirement being tested under ‘Choose between different data stores’.

Learning point: Use a Fabric lakehouse

Question 47

A solution architect reviewing Fourth Coffee’s sales analytics solution asks the customer insights team to support a relational analytics workload that is primarily authored and managed with T-SQL. Since the solution should avoid unnecessary custom code, which recommendation is strongest? Existing users should keep their current access.

Use a Fabric warehouse
Build it in the Visual Query Editor
Handle nulls explicitly with a null-replacement rule such as COALESCE
Use KQL project and where operators
Create a fact table at a defined grain with separate dimension tables

Correct answer: A

Why: A warehouse is optimized for structured relational analytics and T-SQL based development in Fabric. This directly matches the stated requirement.

Option review:

A: A warehouse is optimized for structured relational analytics and T-SQL based development in Fabric. This directly matches the stated requirement.

Learning point: Use a Fabric warehouse

Question 48

The next sprint for Wide World Importers’s risk analytics environment includes a task to ingest and query very high volume event and time-series telemetry with KQL. The acceptance criteria add that the design must preserve a clear development lifecycle. Which Fabric or Power BI action is appropriate? Existing users should keep their current access.

Use an Eventhouse with a KQL database
Use Real-Time hub to browse and connect to real-time data sources
Use the Visual Query Editor to build the query graphically
Apply an explicit predicate that excludes the test rows
Create a fact table at a defined grain with separate dimension tables

Correct answer: A

Why: Eventhouse and KQL databases are optimized for high-volume real-time, log, and time-series analytics. This directly matches the stated requirement.

Option review:

A: Eventhouse and KQL databases are optimized for high-volume real-time, log, and time-series analytics. This directly matches the stated requirement.

B: Real-Time hub is the Fabric experience for discovering, connecting to, and managing real-time event streams and sources. This can be valid for ‘Discover data by using OneLake catalog and Real-Time hub’, but it does not directly satisfy the scenario requirement being tested under ‘Choose between different data stores’.

Learning point: Use an Eventhouse with a KQL database

Question 49

Northwind Traders is troubleshooting a design decision in the service-operations warehouse. The desired end state is to serve curated business measures and relationships efficiently to Power BI reports; the team must avoid granting broader access than required. Which change should the BI platform team make? Existing users should keep their current access.

Configure the appropriate gateway and connection credentials
Append or union the row sets instead of joining them
Create a fact table at a defined grain with separate dimension tables
Use a Power BI semantic model
Write the required SQL query against the endpoint

Correct answer: D

Why: A semantic model is the analytical serving layer for governed relationships, measures, and report consumption. This directly matches the stated requirement.

Option review:

A: An on-premises gateway provides the network bridge while the connection stores the credentials and source details. This can be valid for ‘Create a data connection’, but it does not directly satisfy the scenario requirement being tested under ‘Choose between different data stores’.

D: A semantic model is the analytical serving layer for governed relationships, measures, and report consumption. This directly matches the stated requirement.

Learning point: Use a Power BI semantic model

Question 50

For the executive reporting workspace, Trey Research has documented a business requirement to support Spark engineering over Delta files while also exposing a SQL analytics endpoint for analytics. The security analytics team must meet it in a way where the change must be easy to audit later. What is the best choice? No unrelated workspace or model permissions should be changed.

Create a SQL view
Create the additional dimension or lookup table during transformation
Use a Fabric lakehouse
Use the OneLake catalog to discover and evaluate available data products
Flatten the required lookup attributes into the reporting structure

Correct answer: C

Why: A lakehouse combines OneLake Delta storage with Spark-oriented engineering and a SQL analytics endpoint. This directly matches the stated requirement.

Option review:

A: A view encapsulates a reusable query and presents it as a virtual table. This can be valid for ‘Create views, functions, and stored procedures’, but it does not directly satisfy the scenario requirement being tested under ‘Choose between different data stores’.

B: Adding a curated table can enrich the model with reusable business context for downstream joins and analysis. This can be valid for ‘Enrich data by adding new columns or tables’, but it does not directly satisfy the scenario requirement being tested under ‘Choose between different data stores’.

C: A lakehouse combines OneLake Delta storage with Spark-oriented engineering and a SQL analytics endpoint. This directly matches the stated requirement.

D: The OneLake catalog is designed to discover governed Fabric data items across the organization. This can be valid for ‘Discover data by using OneLake catalog and Real-Time hub’, but it does not directly satisfy the scenario requirement being tested under ‘Choose between different data stores’.

Learning point: Use a Fabric lakehouse

Question 51

A governance review of Alpine Ski House’s customer 360 model asks for evidence that the solution can support a relational analytics workload that is primarily authored and managed with T-SQL. Because the implementation should be easy to troubleshoot, which action should be approved? No unrelated workspace or model permissions should be changed.

Use the Visual Query Editor to build the query graphically
Use Dataflow Gen2 for the ingestion and transformation flow
Use Real-Time hub to browse and connect to real-time data sources
Use a Fabric warehouse
Use KQL operators such as where, summarize, and bin against the KQL database

Correct answer: D

Why: A warehouse is optimized for structured relational analytics and T-SQL based development in Fabric. This directly matches the stated requirement.

Option review:

D: A warehouse is optimized for structured relational analytics and T-SQL based development in Fabric. This directly matches the stated requirement.

E: KQL is optimized for filtering and aggregating log, telemetry, and time-series data in Eventhouse. This can be valid for ‘Select, filter, and aggregate data by using KQL’, but it does not directly satisfy the scenario requirement being tested under ‘Choose between different data stores’.

Learning point: Use a Fabric warehouse

Question 52

Before expanding the sales analytics solution, the customer insights team at Contoso must ingest and query very high volume event and time-series telemetry with KQL. The rollout plan says that the solution should avoid unnecessary custom code. Which option most directly addresses the requirement? No unrelated workspace or model permissions should be changed.

Use the Visual Query Editor to build the query graphically
Model the warehouse with conformed dimensions around fact tables
Create a DAX measure using the required aggregation and filter context logic
Create the additional dimension or lookup table during transformation
Use an Eventhouse with a KQL database

Correct answer: E

Why: Eventhouse and KQL databases are optimized for high-volume real-time, log, and time-series analytics. This directly matches the stated requirement.

Option review:

B: Conformed dimensions and explicit fact grain are core star-schema practices for reliable analytical joins. This can be valid for ‘Implement a star schema for a lakehouse or warehouse’, but it does not directly satisfy the scenario requirement being tested under ‘Choose between different data stores’.

E: Eventhouse and KQL databases are optimized for high-volume real-time, log, and time-series analytics. This directly matches the stated requirement.

Learning point: Use an Eventhouse with a KQL database

Question 53

Wingtip Toys is redesigning its risk analytics environment. The retail insights team must serve curated business measures and relationships efficiently to Power BI reports. In addition, the design must preserve a clear development lifecycle. Which action is the best fit? No unrelated workspace or model permissions should be changed.

Use a Power BI semantic model
Apply the row filter as early as practical in the query or transformation
Group the data by month and region and calculate the required summary measures
Use T-SQL with SELECT, JOIN, WHERE, GROUP BY, and aggregate functions as required
Use a Fabric data pipeline Copy activity

Correct answer: A

Why: A semantic model is the analytical serving layer for governed relationships, measures, and report consumption. This directly matches the stated requirement.

Option review:

A: A semantic model is the analytical serving layer for governed relationships, measures, and report consumption. This directly matches the stated requirement.

C: Aggregation changes detailed rows into grouped summaries at the required analytical grain. This can be valid for ‘Aggregate data’, but it does not directly satisfy the scenario requirement being tested under ‘Choose between different data stores’.

E: Copy activity is suited to orchestrated bulk data movement into Fabric with scheduling and monitoring. This can be valid for ‘Ingest or access data as needed’, but it does not directly satisfy the scenario requirement being tested under ‘Choose between different data stores’.

Learning point: Use a Power BI semantic model

Question 54

During a design review for Proseware’s service-operations warehouse, one requirement is non-negotiable: support Spark engineering over Delta files while also exposing a SQL analytics endpoint for analytics. Because the team must avoid granting broader access than required, what should the BI platform team implement? The team will validate the change first in a nonproduction environment.

Precompute the required daily aggregate table or result
Write the required SQL query against the endpoint
Use a Fabric lakehouse
Build it in the Visual Query Editor
Create the additional dimension or lookup table during transformation

Correct answer: C

Why: A lakehouse combines OneLake Delta storage with Spark-oriented engineering and a SQL analytics endpoint. This directly matches the stated requirement.

Option review:

C: A lakehouse combines OneLake Delta storage with Spark-oriented engineering and a SQL analytics endpoint. This directly matches the stated requirement.

Learning point: Use a Fabric lakehouse

Question 55

The security analytics team at Blue Yonder Airlines is preparing the next release of its executive reporting workspace. They need to support a relational analytics workload that is primarily authored and managed with T-SQL; the change must be easy to audit later. Which choice most directly satisfies the requirement? The team will validate the change first in a nonproduction environment.

Precompute the required daily aggregate table or result
Use KQL operators such as where, summarize, and bin against the KQL database
Convert the column to an appropriate date or datetime data type
Use a Fabric warehouse
Create a OneLake shortcut to the existing data

Correct answer: D

Why: A warehouse is optimized for structured relational analytics and T-SQL based development in Fabric. This directly matches the stated requirement.

Option review:

D: A warehouse is optimized for structured relational analytics and T-SQL based development in Fabric. This directly matches the stated requirement.

Learning point: Use a Fabric warehouse

Question 56

A production readiness review at Fabrikam found a gap in the customer 360 model. The remediation must ingest and query very high volume event and time-series telemetry with KQL, and the implementation should be easy to troubleshoot. What is the most appropriate action? The team will validate the change first in a nonproduction environment.

Use Dataflow Gen2 for the ingestion and transformation flow
Use an Eventhouse with a KQL database
Create a DAX measure using the required aggregation and filter context logic
Identify the duplicate keys and deduplicate according to the authoritative business rule
Group the data by month and region and calculate the required summary measures

Correct answer: B

Why: Eventhouse and KQL databases are optimized for high-volume real-time, log, and time-series analytics. This directly matches the stated requirement.

Option review:

A: Dataflow Gen2 provides a Power Query based low-code ingestion and transformation experience. This can be valid for ‘Ingest or access data as needed’, but it does not directly satisfy the scenario requirement being tested under ‘Choose between different data stores’.

B: Eventhouse and KQL databases are optimized for high-volume real-time, log, and time-series analytics. This directly matches the stated requirement.

E: Aggregation changes detailed rows into grouped summaries at the required analytical grain. This can be valid for ‘Aggregate data’, but it does not directly satisfy the scenario requirement being tested under ‘Choose between different data stores’.

Learning point: Use an Eventhouse with a KQL database

Question 57

For a new phase of the sales analytics solution, Litware asks the customer insights team to serve curated business measures and relationships efficiently to Power BI reports. The architecture decision record also states that the solution should avoid unnecessary custom code. Which approach should be selected? The team will validate the change first in a nonproduction environment.

Create a stored procedure
Use Real-Time hub to browse and connect to real-time data sources
Create a SQL view
Use a Power BI semantic model
Flatten the required lookup attributes into the reporting structure

Correct answer: D

Why: A semantic model is the analytical serving layer for governed relationships, measures, and report consumption. This directly matches the stated requirement.

Option review:

A: A stored procedure packages procedural multi-statement T-SQL operations for repeatable execution. This can be valid for ‘Create views, functions, and stored procedures’, but it does not directly satisfy the scenario requirement being tested under ‘Choose between different data stores’.

D: A semantic model is the analytical serving layer for governed relationships, measures, and report consumption. This directly matches the stated requirement.

Learning point: Use a Power BI semantic model

Question 58

Woodgrove Bank is standardizing how the risk analytics environment is managed. The immediate goal is to support Spark engineering over Delta files while also exposing a SQL analytics endpoint for analytics. Given that the design must preserve a clear development lifecycle, which option should the retail insights team choose? The solution must work with the current Fabric architecture rather than a parallel custom platform.

Group the data by month and region and calculate the required summary measures
Use a DAX query with functions such as SUMMARIZECOLUMNS and CALCULATE as appropriate
Use a Fabric lakehouse
Add a calculated or derived column in the transformation layer
Configure the appropriate gateway and connection credentials

Correct answer: C

Why: A lakehouse combines OneLake Delta storage with Spark-oriented engineering and a SQL analytics endpoint. This directly matches the stated requirement.

Option review:

C: A lakehouse combines OneLake Delta storage with Spark-oriented engineering and a SQL analytics endpoint. This directly matches the stated requirement.

D: A derived column enriches the dataset with business logic while preserving the source fields. This can be valid for ‘Enrich data by adding new columns or tables’, but it does not directly satisfy the scenario requirement being tested under ‘Choose between different data stores’.

E: An on-premises gateway provides the network bridge while the connection stores the credentials and source details. This can be valid for ‘Create a data connection’, but it does not directly satisfy the scenario requirement being tested under ‘Choose between different data stores’.

Learning point: Use a Fabric lakehouse

Question 59

An internal audit of Coho Winery’s service-operations warehouse identifies this requirement: support a relational analytics workload that is primarily authored and managed with T-SQL. The BI platform team also notes that the team must avoid granting broader access than required. What should they do? The solution must work with the current Fabric architecture rather than a parallel custom platform.

Use a Fabric warehouse
Handle nulls explicitly with a null-replacement rule such as COALESCE
Use a Fabric data pipeline Copy activity
Enable the supported OneLake availability or integration for the Eventhouse data
Use the Visual Query Editor to build the query graphically

Correct answer: A

Why: A warehouse is optimized for structured relational analytics and T-SQL based development in Fabric. This directly matches the stated requirement.

Option review:

A: A warehouse is optimized for structured relational analytics and T-SQL based development in Fabric. This directly matches the stated requirement.

D: OneLake integration makes supported Eventhouse data discoverable and accessible through OneLake for cross-engine use. This can be valid for ‘Implement OneLake integration for Eventhouse and semantic models’, but it does not directly satisfy the scenario requirement being tested under ‘Choose between different data stores’.

Learning point: Use a Fabric warehouse

Question 60

The executive reporting workspace at Adventure Works is moving from proof of concept to production. Before rollout, the security analytics team must ingest and query very high volume event and time-series telemetry with KQL, while ensuring that the change must be easy to audit later. Which action best meets both needs? The solution must work with the current Fabric architecture rather than a parallel custom platform.

Use an Eventhouse with a KQL database
Use Dataflow Gen2 for the ingestion and transformation flow
Use the OneLake catalog to discover and evaluate available data products
Model the warehouse with conformed dimensions around fact tables
Create a DAX measure using the required aggregation and filter context logic

Correct answer: A

Why: Eventhouse and KQL databases are optimized for high-volume real-time, log, and time-series analytics. This directly matches the stated requirement.

Option review:

A: Eventhouse and KQL databases are optimized for high-volume real-time, log, and time-series analytics. This directly matches the stated requirement.

C: The OneLake catalog is designed to discover governed Fabric data items across the organization. This can be valid for ‘Discover data by using OneLake catalog and Real-Time hub’, but it does not directly satisfy the scenario requirement being tested under ‘Choose between different data stores’.

Learning point: Use an Eventhouse with a KQL database

Question 61

Tailspin Toys has a change request for the marketing semantic model: make Eventhouse data available in OneLake for downstream Fabric engines without building a separate export pipeline. The enterprise reporting group wants a solution where the implementation should be easy to troubleshoot. Which implementation is most suitable? Existing users should keep their current access.

Enable the supported OneLake availability or integration for the Eventhouse data
Use KQL operators such as where, summarize, and bin against the KQL database
Cast the column to the appropriate numeric type before aggregation
Use a Fabric warehouse
Create a OneLake shortcut to the existing data

Correct answer: A

Why: OneLake integration makes supported Eventhouse data discoverable and accessible through OneLake for cross-engine use. This directly matches the stated requirement.

Option review:

A: OneLake integration makes supported Eventhouse data discoverable and accessible through OneLake for cross-engine use. This directly matches the stated requirement.

C: Numeric calculations require a compatible numeric type and should avoid relying on implicit conversion. This can be valid for ‘Convert column data types’, but it does not directly satisfy the scenario requirement being tested under ‘Implement OneLake integration for Eventhouse and semantic models’.

D: A warehouse is optimized for structured relational analytics and T-SQL based development in Fabric. This can be valid for ‘Choose between different data stores’, but it does not directly satisfy the scenario requirement being tested under ‘Implement OneLake integration for Eventhouse and semantic models’.

Learning point: Enable the supported OneLake availability or integration for the Eventhouse data

Question 62

A solution architect reviewing Fourth Coffee’s supply-chain lakehouse asks the finance analytics squad to build a semantic model over OneLake Delta data without importing a separate copy into the model. Since the solution should avoid unnecessary custom code, which recommendation is strongest? Existing users should keep their current access.

Use Direct Lake against the OneLake-backed data
Use KQL project and where operators
Create a fact table at a defined grain with separate dimension tables
Add a calculated or derived column in the transformation layer
Use an inner join on the matching business or surrogate key

Correct answer: A

Why: Direct Lake lets semantic models query OneLake Delta data with in-memory analytical performance without a traditional import copy. This directly matches the stated requirement.

Option review:

A: Direct Lake lets semantic models query OneLake Delta data with in-memory analytical performance without a traditional import copy. This directly matches the stated requirement.

B: KQL provides concise operators for selecting columns and filtering event data. This can be valid for ‘Select, filter, and aggregate data by using KQL’, but it does not directly satisfy the scenario requirement being tested under ‘Implement OneLake integration for Eventhouse and semantic models’.

E: An inner join returns only rows with matching keys on both sides. This can be valid for ‘Merge or join data’, but it does not directly satisfy the scenario requirement being tested under ‘Implement OneLake integration for Eventhouse and semantic models’.

Learning point: Use Direct Lake against the OneLake-backed data

Question 63

The next sprint for Wide World Importers’s IoT telemetry solution includes a task to make Eventhouse data available in OneLake for downstream Fabric engines without building a separate export pipeline. The acceptance criteria add that the design must preserve a clear development lifecycle. Which Fabric or Power BI action is appropriate? No unrelated workspace or model permissions should be changed.

Cast the column to the appropriate numeric type before aggregation
Create a SQL view
Build it in the Visual Query Editor
Enable the supported OneLake availability or integration for the Eventhouse data
Use KQL project and where operators

Correct answer: D

Why: OneLake integration makes supported Eventhouse data discoverable and accessible through OneLake for cross-engine use. This directly matches the stated requirement.

Option review:

A: Numeric calculations require a compatible numeric type and should avoid relying on implicit conversion. This can be valid for ‘Convert column data types’, but it does not directly satisfy the scenario requirement being tested under ‘Implement OneLake integration for Eventhouse and semantic models’.

B: A view encapsulates a reusable query and presents it as a virtual table. This can be valid for ‘Create views, functions, and stored procedures’, but it does not directly satisfy the scenario requirement being tested under ‘Implement OneLake integration for Eventhouse and semantic models’.

D: OneLake integration makes supported Eventhouse data discoverable and accessible through OneLake for cross-engine use. This directly matches the stated requirement.

E: KQL provides concise operators for selecting columns and filtering event data. This can be valid for ‘Select, filter, and aggregate data by using KQL’, but it does not directly satisfy the scenario requirement being tested under ‘Implement OneLake integration for Eventhouse and semantic models’.

Learning point: Enable the supported OneLake availability or integration for the Eventhouse data

Question 64

Northwind Traders is troubleshooting a design decision in the finance reporting platform. The desired end state is to build a semantic model over OneLake Delta data without importing a separate copy into the model; the team must avoid granting broader access than required. Which change should the operations data team make? No unrelated workspace or model permissions should be changed.

Use KQL operators such as where, summarize, and bin against the KQL database
Use Direct Lake against the OneLake-backed data
Use an inner join on the matching business or surrogate key
Apply an explicit predicate that excludes the test rows
Create the additional dimension or lookup table during transformation

Correct answer: B

Why: Direct Lake lets semantic models query OneLake Delta data with in-memory analytical performance without a traditional import copy. This directly matches the stated requirement.

Option review:

A: KQL is optimized for filtering and aggregating log, telemetry, and time-series data in Eventhouse. This can be valid for ‘Select, filter, and aggregate data by using KQL’, but it does not directly satisfy the scenario requirement being tested under ‘Implement OneLake integration for Eventhouse and semantic models’.

B: Direct Lake lets semantic models query OneLake Delta data with in-memory analytical performance without a traditional import copy. This directly matches the stated requirement.

Learning point: Use Direct Lake against the OneLake-backed data

Question 65

For the retail performance dashboard, Trey Research has documented a business requirement to make Eventhouse data available in OneLake for downstream Fabric engines without building a separate export pipeline. The data governance group must meet it in a way where the change must be easy to audit later. What is the best choice? The team will validate the change first in a nonproduction environment.

Use an inner join on the matching business or surrogate key
Use Real-Time hub to browse and connect to real-time data sources
Write the required SQL query against the endpoint
Enable the supported OneLake availability or integration for the Eventhouse data
Cast the column to the appropriate numeric type before aggregation

Correct answer: D

Why: OneLake integration makes supported Eventhouse data discoverable and accessible through OneLake for cross-engine use. This directly matches the stated requirement.

Option review:

A: An inner join returns only rows with matching keys on both sides. This can be valid for ‘Merge or join data’, but it does not directly satisfy the scenario requirement being tested under ‘Implement OneLake integration for Eventhouse and semantic models’.

D: OneLake integration makes supported Eventhouse data discoverable and accessible through OneLake for cross-engine use. This directly matches the stated requirement.

Learning point: Enable the supported OneLake availability or integration for the Eventhouse data

Question 66

A governance review of Alpine Ski House’s marketing semantic model asks for evidence that the solution can build a semantic model over OneLake Delta data without importing a separate copy into the model. Because the implementation should be easy to troubleshoot, which action should be approved? The team will validate the change first in a nonproduction environment.

Use Direct Lake against the OneLake-backed data
Add a calculated or derived column in the transformation layer
Create a fact table at a defined grain with separate dimension tables
Use T-SQL with SELECT, JOIN, WHERE, GROUP BY, and aggregate functions as required
Model the warehouse with conformed dimensions around fact tables

Correct answer: A

Why: Direct Lake lets semantic models query OneLake Delta data with in-memory analytical performance without a traditional import copy. This directly matches the stated requirement.

Option review:

A: Direct Lake lets semantic models query OneLake Delta data with in-memory analytical performance without a traditional import copy. This directly matches the stated requirement.

Learning point: Use Direct Lake against the OneLake-backed data

Question 67

Before expanding the supply-chain lakehouse, the finance analytics squad at Contoso must make Eventhouse data available in OneLake for downstream Fabric engines without building a separate export pipeline. The rollout plan says that the solution should avoid unnecessary custom code. Which option most directly addresses the requirement? The solution must work with the current Fabric architecture rather than a parallel custom platform.

Use a left outer join from customers to orders
Create the additional dimension or lookup table during transformation
Filter or remediate rows with missing mandatory keys before loading the curated layer
Enable the supported OneLake availability or integration for the Eventhouse data
Create a OneLake shortcut to the existing data

Correct answer: D

Why: OneLake integration makes supported Eventhouse data discoverable and accessible through OneLake for cross-engine use. This directly matches the stated requirement.

Option review:

D: OneLake integration makes supported Eventhouse data discoverable and accessible through OneLake for cross-engine use. This directly matches the stated requirement.

Learning point: Enable the supported OneLake availability or integration for the Eventhouse data

Question 68

Wingtip Toys is redesigning its IoT telemetry solution. The analytics engineering team must build a semantic model over OneLake Delta data without importing a separate copy into the model. In addition, the design must preserve a clear development lifecycle. Which action is the best fit? The solution must work with the current Fabric architecture rather than a parallel custom platform.

Create a DAX measure using the required aggregation and filter context logic
Model the warehouse with conformed dimensions around fact tables
Use Direct Lake against the OneLake-backed data
Use the Visual Query Editor to build the query graphically
Group the data by month and region and calculate the required summary measures

Correct answer: C

Why: Direct Lake lets semantic models query OneLake Delta data with in-memory analytical performance without a traditional import copy. This directly matches the stated requirement.

Option review:

A: DAX measures evaluate in semantic-model filter context and are the native way to define reusable analytical calculations. This can be valid for ‘Select, filter, and aggregate data by using DAX’, but it does not directly satisfy the scenario requirement being tested under ‘Implement OneLake integration for Eventhouse and semantic models’.

C: Direct Lake lets semantic models query OneLake Delta data with in-memory analytical performance without a traditional import copy. This directly matches the stated requirement.

Learning point: Use Direct Lake against the OneLake-backed data

Question 69

During a design review for Proseware’s finance reporting platform, one requirement is non-negotiable: make Eventhouse data available in OneLake for downstream Fabric engines without building a separate export pipeline. Because the team must avoid granting broader access than required, what should the operations data team implement? The design decision will be reviewed by both data engineering and BI owners.

Filter or remediate rows with missing mandatory keys before loading the curated layer
Group the data by month and region and calculate the required summary measures
Create a SQL function
Enable the supported OneLake availability or integration for the Eventhouse data
Use a Fabric lakehouse

Correct answer: D

Why: OneLake integration makes supported Eventhouse data discoverable and accessible through OneLake for cross-engine use. This directly matches the stated requirement.

Option review:

C: A function is appropriate for reusable parameterized logic that returns a value or table expression. This can be valid for ‘Create views, functions, and stored procedures’, but it does not directly satisfy the scenario requirement being tested under ‘Implement OneLake integration for Eventhouse and semantic models’.

D: OneLake integration makes supported Eventhouse data discoverable and accessible through OneLake for cross-engine use. This directly matches the stated requirement.

Learning point: Enable the supported OneLake availability or integration for the Eventhouse data

Question 70

The data governance group at Blue Yonder Airlines is preparing the next release of its retail performance dashboard. They need to build a semantic model over OneLake Delta data without importing a separate copy into the model; the change must be easy to audit later. Which choice most directly satisfies the requirement? The design decision will be reviewed by both data engineering and BI owners.

Apply the row filter as early as practical in the query or transformation
Denormalize selected attributes into the analytical table
Group the data by month and region and calculate the required summary measures
Use Real-Time hub to browse and connect to real-time data sources
Use Direct Lake against the OneLake-backed data

Correct answer: E

Why: Direct Lake lets semantic models query OneLake Delta data with in-memory analytical performance without a traditional import copy. This directly matches the stated requirement.

Option review:

E: Direct Lake lets semantic models query OneLake Delta data with in-memory analytical performance without a traditional import copy. This directly matches the stated requirement.

Learning point: Use Direct Lake against the OneLake-backed data

Question 71

A production readiness review at Fabrikam found a gap in the marketing semantic model. The remediation must make Eventhouse data available in OneLake for downstream Fabric engines without building a separate export pipeline, and the implementation should be easy to troubleshoot. What is the most appropriate action? Existing users should keep their current access.

Enable the supported OneLake availability or integration for the Eventhouse data
Denormalize selected attributes into the analytical table
Create a SQL view
Use T-SQL with SELECT, JOIN, WHERE, GROUP BY, and aggregate functions as required
Use a Fabric data pipeline Copy activity

Correct answer: A

Why: OneLake integration makes supported Eventhouse data discoverable and accessible through OneLake for cross-engine use. This directly matches the stated requirement.

Option review:

A: OneLake integration makes supported Eventhouse data discoverable and accessible through OneLake for cross-engine use. This directly matches the stated requirement.

C: A view encapsulates a reusable query and presents it as a virtual table. This can be valid for ‘Create views, functions, and stored procedures’, but it does not directly satisfy the scenario requirement being tested under ‘Implement OneLake integration for Eventhouse and semantic models’.

Learning point: Enable the supported OneLake availability or integration for the Eventhouse data

Question 72

For a new phase of the supply-chain lakehouse, Litware asks the finance analytics squad to build a semantic model over OneLake Delta data without importing a separate copy into the model. The architecture decision record also states that the solution should avoid unnecessary custom code. Which approach should be selected? Existing users should keep their current access.

Use a left outer join from customers to orders
Add a calculated or derived column in the transformation layer
Use Direct Lake against the OneLake-backed data
Create a DAX measure using the required aggregation and filter context logic
Group the data by month and region and calculate the required summary measures

Correct answer: C

Why: Direct Lake lets semantic models query OneLake Delta data with in-memory analytical performance without a traditional import copy. This directly matches the stated requirement.

Option review:

C: Direct Lake lets semantic models query OneLake Delta data with in-memory analytical performance without a traditional import copy. This directly matches the stated requirement.

D: DAX measures evaluate in semantic-model filter context and are the native way to define reusable analytical calculations. This can be valid for ‘Select, filter, and aggregate data by using DAX’, but it does not directly satisfy the scenario requirement being tested under ‘Implement OneLake integration for Eventhouse and semantic models’.

Learning point: Use Direct Lake against the OneLake-backed data

Question 73

Woodgrove Bank is standardizing how the IoT telemetry solution is managed. The immediate goal is to make Eventhouse data available in OneLake for downstream Fabric engines without building a separate export pipeline. Given that the design must preserve a clear development lifecycle, which option should the analytics engineering team choose? No unrelated workspace or model permissions should be changed.

Add a calculated or derived column in the transformation layer
Create a DAX measure using the required aggregation and filter context logic
Enable the supported OneLake availability or integration for the Eventhouse data
Apply the row filter as early as practical in the query or transformation
Use a Fabric data pipeline Copy activity

Correct answer: C

Why: OneLake integration makes supported Eventhouse data discoverable and accessible through OneLake for cross-engine use. This directly matches the stated requirement.

Option review:

A: A derived column enriches the dataset with business logic while preserving the source fields. This can be valid for ‘Enrich data by adding new columns or tables’, but it does not directly satisfy the scenario requirement being tested under ‘Implement OneLake integration for Eventhouse and semantic models’.

B: DAX measures evaluate in semantic-model filter context and are the native way to define reusable analytical calculations. This can be valid for ‘Select, filter, and aggregate data by using DAX’, but it does not directly satisfy the scenario requirement being tested under ‘Implement OneLake integration for Eventhouse and semantic models’.

C: OneLake integration makes supported Eventhouse data discoverable and accessible through OneLake for cross-engine use. This directly matches the stated requirement.

D: Early filtering reduces the volume of data moved and processed while preserving only the rows required by the workload. This can be valid for ‘Filter data’, but it does not directly satisfy the scenario requirement being tested under ‘Implement OneLake integration for Eventhouse and semantic models’.

Learning point: Enable the supported OneLake availability or integration for the Eventhouse data

Question 74

An internal audit of Coho Winery’s finance reporting platform identifies this requirement: build a semantic model over OneLake Delta data without importing a separate copy into the model. The operations data team also notes that the team must avoid granting broader access than required. What should they do? No unrelated workspace or model permissions should be changed.

Use a left outer join from customers to orders
Use Direct Lake against the OneLake-backed data
Create a stored procedure
Build it in the Visual Query Editor
Use KQL operators such as where, summarize, and bin against the KQL database

Correct answer: B

Why: Direct Lake lets semantic models query OneLake Delta data with in-memory analytical performance without a traditional import copy. This directly matches the stated requirement.

Option review:

B: Direct Lake lets semantic models query OneLake Delta data with in-memory analytical performance without a traditional import copy. This directly matches the stated requirement.

D: The visual editor is intended for graphical query construction while still supporting common relational operations. This can be valid for ‘Select, filter, and aggregate data by using the Visual query editor’, but it does not directly satisfy the scenario requirement being tested under ‘Implement OneLake integration for Eventhouse and semantic models’.

Learning point: Use Direct Lake against the OneLake-backed data

Question 75

The retail performance dashboard at Adventure Works is moving from proof of concept to production. Before rollout, the data governance group must make Eventhouse data available in OneLake for downstream Fabric engines without building a separate export pipeline, while ensuring that the change must be easy to audit later. Which action best meets both needs? The team will validate the change first in a nonproduction environment.

Enable the supported OneLake availability or integration for the Eventhouse data
Use KQL operators such as where, summarize, and bin against the KQL database
Use Dataflow Gen2 for the ingestion and transformation flow
Model the warehouse with conformed dimensions around fact tables
Denormalize selected attributes into the analytical table

Correct answer: A

Why: OneLake integration makes supported Eventhouse data discoverable and accessible through OneLake for cross-engine use. This directly matches the stated requirement.

Option review:

A: OneLake integration makes supported Eventhouse data discoverable and accessible through OneLake for cross-engine use. This directly matches the stated requirement.

Learning point: Enable the supported OneLake availability or integration for the Eventhouse data

Question 1

Question 2

Question 3

Question 4

Question 5

Question 6

Question 7

Question 8

Question 9

Question 10

Question 11

Question 12

Question 13

Question 14

Question 15

Question 16

Question 17

Question 18

Question 19

Question 20

Question 21

Question 22

Question 23

Question 24

Question 25

Question 26

Question 27

Question 28

Question 29

Question 30

Question 31

Question 32

Question 33

Question 34

Question 35

Question 36

Question 37

Question 38

Question 39

Question 40

Question 41

Question 42

Question 43

Question 44

Question 45

Question 46

Question 47

Question 48

Question 49

Question 50

Question 51

Question 52

Question 53

Question 54

Question 55

Question 56

Question 57

Question 58

Question 59

Question 60

Question 61

Question 62

Question 63

Question 64

Question 65

Question 66

Question 67

Question 68

Question 69

Question 70

Question 71

Question 72

Question 73

Question 74

Question 75

Related posts: