Microsoft DP-700 Practice Test Questions, Exam Dumps

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DP-700 Microsoft Practice Test Questions and Exam Dumps

Question No 1:

What should you do to ensure that data analysts can access the gold layer lakehouse?

A. Add the DataAnalyst group to the Viewer role for WorkspaceA.
B. Share the lake house with the DataAnalysts group and grant the "Build reports on the default semantic model" permission.
C. Share the lake house with the DataAnalysts group and grant the "Read all SQL Endpoint data" permission.
D. Share the lake house with the DataAnalysts group and grant the "Read all Apache Spark" permission.

Answer:

B. Share the lake house with the DataAnalysts group and grant the "Build reports on the default semantic model" permission.

Explanation:

In this case study, the goal is to allow data analysts to access the gold layer of the lake house while adhering to strict security and data governance requirements.

The gold layer of the lakehouse is intended for analytical queries and is the final stage in the medallion architecture, where cleaned and transformed data is made available for users to analyze. The company has a security requirement that data analysts should only have read access to the gold layer and should not access raw or intermediate data in the bronze and silver layers. Therefore, the solution must enable access to the gold layer without compromising the data security model.

Let’s break down the options:

Option A: Add the DataAnalyst group to the Viewer role for WorkspaceA
Adding the DataAnalyst group to the Viewer role grants read-only access to all items within WorkspaceA. However, this does not specifically grant access to the gold layer lakehouse and its data. This approach is too broad and doesn’t meet the requirement of limiting access to only the gold layer.
Option B: Share the lake house with the DataAnalysts group and grant the "Build reports on the default semantic model" permission
This is the correct approach. By sharing the lake house with the DataAnalysts group and granting them permission to build reports on the semantic model in the gold layer, data analysts can access the necessary data for reporting while ensuring they are restricted from accessing other layers (bronze and silver). This aligns with the company's security model and allows analysts to use the data for their analytical purposes.
Option C: Share the lake house with the DataAnalysts group and grant the "Read all SQL Endpoint data" permission
This option is not suitable because it provides broader access to SQL endpoint data, which is not necessary for the data analysts' role and could allow access to data from other layers beyond the gold layer.
Option D: Share the lake house with the DataAnalysts group and grant the "Read all Apache Spark" permission
Granting "Read all Apache Spark" permissions is unnecessary and too broad for the use case, as it allows access to all Apache Spark-based operations, which is more than required for accessing the gold layer.

Therefore, the correct choice is Option B, as it specifically grants the necessary access for data analysts to build reports based on the semantic model in the gold layer, maintaining the security and governance requirements.

Question No 2:

You are working with a Fabric workspace and have semi-structured data. You need to read this data using T-SQL, KQL, and Apache Spark, while ensuring that the data will only be written using Spark. What should you use to store the data?

A. A lakehouse
B. An eventhouse
C. A datamart
D. A warehouse

Answer: A. A lakehouse

Explanation:

In this scenario, the best solution for storing semi-structured data that can be accessed by T-SQL, KQL, and Apache Spark, while being written exclusively through Spark, is a lakehouse. A lake house combines elements of both a data lake and a data warehouse, making it an ideal choice for working with large volumes of semi-structured and structured data, especially when multiple querying tools are involved.

Here’s why the lakehouse is the correct choice:

Semi-Structured Data: A lake house allows you to store raw data in its original format (often in open file formats like Parquet or Delta) while also supporting structured data. This is important because semi-structured data (such as JSON, Avro, or XML) can be read and processed efficiently by multiple systems, including Apache Spark, which is mentioned as the data writer in your scenario.
Support for Multiple Querying Systems: The lakehouse supports T-SQL for traditional relational querying, KQL (Kusto Query Language) for log and telemetry data analysis, and Apache Spark for distributed processing. This makes it versatile for different data access requirements.
Data Writing via Spark: In a lake house architecture, data is often written and processed using Apache Spark, which is a powerful engine for handling big data workloads. Writing data exclusively through Spark aligns with the typical use case for lake houses, where Spark performs batch and streaming operations on the data.

Let’s briefly explore the other options:

Option B: Eventhouse is not a standard or widely recognized term for data storage solutions, so it is not suitable for this scenario.
Option C: Data Mart is a specialized database used for analytical purposes, often for a specific business area or department. It is typically not designed for handling semi-structured data or providing the flexibility that a lakehouse offers.
Option D: Warehouse refers to a data warehouse, which is designed for structured data and optimized for querying large datasets. While it can handle structured data well, it is less efficient for semi-structured data compared to a lakehouse.

In conclusion, a lakehouse provides the best storage solution for the combination of semi-structured data, multiple querying interfaces, and the use of Apache Spark for writing data.

Question No 3:

You are working in a Fabric workspace containing a warehouse named Warehouse1. You have an on-premises Microsoft SQL Server database named Database1, accessed using an on-premises data gateway.You need to copy data from Database1 to Warehouse1.

Which item should you use?

A. A Dataflow Gen1 dataflow
B. A data pipeline
C. A KQL query set
D. A notebook

Answer: B. A data pipeline

Explanation:

In this scenario, you're working with a data warehouse (Warehouse1) in a Fabric workspace, and your task is to copy data from an on-premises SQL Server database (Database1) to the warehouse. The best approach to achieve this is by using a data pipeline.

A data pipeline is a series of steps that can ingest, transform, and load data into a destination like a data warehouse. When dealing with on-premises databases, a data pipeline can leverage the on-premises data gateway to connect to the SQL Server database and move data into the warehouse.

Let’s break down why the other options are not the most suitable:

A. A Dataflow Gen1 dataflow: Dataflows in this context are primarily used for transforming data and moving it from one source to another, typically in a more granular, step-by-step fashion. However, Dataflows are not typically designed for robust ETL tasks across complex environments or large-scale batch operations. It's better suited for simpler scenarios and transformation logic, not specifically for ingesting data from an on-premises database to a warehouse.
C. A KQL query set: KQL (Kusto Query Language) is generally used for querying and analyzing large datasets in Azure Data Explorer or Log Analytics, not for performing data extraction or loading tasks into a warehouse. This option does not fit the requirements.
D. A notebook: Notebooks are great for interactive data analysis, writing code, and running queries in a collaborative environment. However, they are not designed for data movement tasks like copying data from a SQL Server database to a warehouse. Notebooks focus on data exploration and analysis rather than on large-scale data ingestion.

Thus, a data pipeline is the most appropriate tool for copying data from an on-premises SQL Server database to the Fabric data warehouse, providing the right integration with the data gateway and data movement capabilities.

Question No 4:

You are working in a Fabric workspace containing a warehouse named Warehouse1. You have an on-premises Microsoft SQL Server database named Database1, accessed using an on-premises data gateway.You need to copy data from Database1 to Warehouse1.

Which item should you use?

A. An Apache Spark job definition
B. A data pipeline
C. A Dataflow Gen1 dataflow
D. An event stream

Answer: B. A data pipeline

Explanation:

This question is similar to the previous one and follows the same reasoning. A data pipeline is designed to move data from one location to another, and in this case, it would efficiently copy data from the on-premises SQL Server database to the warehouse.

A. An Apache Spark job definition: Apache Spark jobs are used for running distributed data processing tasks and analytics. While Spark can be used to process data, it's not typically the best choice for simply copying data from one location to another. Spark jobs are more suited for heavy data processing and analytics tasks rather than basic ETL or data ingestion.
C. A Dataflow Gen1 dataflow: Similar to the previous explanation, Dataflows are more focused on data transformation and enrichment rather than direct data ingestion. While you could technically use a dataflow to move data, a data pipeline is a more efficient and scalable tool for copying data from an on-premises SQL Server database.
D. An event stream: Event streams are used for processing real-time data, often in scenarios involving data that arrives continuously. They are not designed for bulk data movement like copying data from a SQL database to a data warehouse.

Therefore, a data pipeline is still the best choice here for transferring data from Database1 to Warehouse1.

Question No 5:

You have a Fabric F32 capacity containing a workspace with a warehouse named DW1. The warehouse is modeled using MD5 hash surrogate keys. Over the past year, DW1 has grown from 200 million rows to 500 million rows.
You have Microsoft Power BI reports based on Direct Lake. Users report degraded performance, and some visuals show errors. You need to resolve the performance issues. The solution must provide the best query performance while minimizing operational costs.

Which should you do?

A. Change the MD5 hash to SHA256
B. Increase the capacity
C. Enable V-Order
D. Modify the surrogate keys to use a different data type
E. Create views

Answer: C. Enable V-Order

Explanation:

In this scenario, you are facing performance issues in a large-scale data warehouse with a significant growth in data volume. The performance degradation in Power BI reports, especially with Direct Lake connections, is likely due to inefficiencies in data querying.

V-Order is a technique that optimizes the data storage and indexing in a way that aligns with the query patterns, improving query performance. Enabling V-Order improves performance by organizing data in the warehouse based on the most commonly queried columns. This reduces the need for expensive table scans and enhances performance during query execution.

Let’s review the other options:

A. Change the MD5 hash to SHA256: Changing the hash type will not necessarily improve performance. While SHA256 may provide more security, it will increase the size of the surrogate keys, which can actually degrade performance, not improve it.
B. Increase the capacity: Increasing the capacity may help handle larger datasets, but it will not directly address the performance issues related to query execution or data indexing. Operational costs would also increase with more capacity, so this approach may not minimize costs.
D. Modify the surrogate keys to use a different data type: Changing the surrogate key data type might reduce storage space but will not directly improve query performance, especially if the performance issues are tied to how the data is structured or queried.
E. Create views: While views can simplify queries, they don't inherently improve performance. In large datasets, views may even lead to more complicated queries that are slower, so this is not the best option for resolving performance degradation.

V-Order provides the most direct solution to optimizing query performance while keeping operational costs lower. By optimizing the order in which data is stored, it reduces the resources needed to query large datasets, making it the best choice.

Question No 6:

You have a Fabric workspace named Workspace1 that contains a notebook named Notebook1. In Workspace1, you create a new notebook named Notebook2. You need to ensure that you can attach Notebook2 to the same Apache Spark session as Notebook1.

What should you do?

A. Enable high concurrency for notebooks.
B. Enable dynamic allocation for the Spark pool.
C. Change the runtime version.
D. Increase the number of executors.

Answer: A. Enable high concurrency for notebooks.

Explanation:

In a Fabric workspace, to ensure that multiple notebooks are connected to the same Apache Spark session, you need to enable high concurrency. This setting ensures that the Spark session remains active and available to multiple notebooks simultaneously, enabling them to attach to the same session. High concurrency allows notebooks to share the Spark session for running jobs, which is crucial when multiple notebooks need to access and work with the same data or computing resources.

In contrast, dynamic allocation for the Spark pool (Option B) is more focused on resource management and automatically adjusting resources based on the workload, which does not directly relate to connecting notebooks to the same session. Changing the runtime version (Option C) is necessary for ensuring compatibility with specific Spark features or libraries but does not impact the ability to connect notebooks to the same session. Increasing the number of executors (Option D) may improve resource distribution and performance but does not directly address the issue of session attachment across notebooks.

Thus, enabling high concurrency is the most effective method for ensuring that both notebooks can operate within the same Apache Spark session.

Question No 7:

You have a Fabric workspace named Workspace1 that contains a lakehouse named Lakehouse1. Lakehouse1 contains the following tables:

Orders
Customer
Employee

The Employee table contains Personally Identifiable Information (PII). A data engineer is building a workflow that requires writing data to the Customer table, however, the user does NOT have the elevated permissions required to view the contents of the Employee table. You need to ensure that the data engineer can write data to the Customer table without reading data from the Employee table.

Which three actions should you perform? Each correct answer presents part of the solution.

A. Share Lakehouse1 with the data engineer.
B. Assign the data engineer the Contributor role for Workspace2.
C. Assign the data engineer the Viewer role for Workspace2.
D. Assign the data engineer the Contributor role for Workspace1.
E. Migrate the Employee table from Lakehouse1 to Lakehouse2.
F. Create a new workspace named Workspace2 that contains a new lakehouse named Lakehouse2.
G. Assign the data engineer the Viewer role for Workspace1.

Answer: A, D, E.

Explanation:

In this scenario, to ensure the data engineer can write data to the Customer table without accessing the Employee table containing PII, the following actions are needed:

A. Share Lakehouse1 with the data engineer: This ensures that the data engineer has access to the lakehouse and can interact with its tables, including the Customer table.
D. Assign the data engineer the Contributor role for Workspace1: The Contributor role allows the data engineer to modify and write data to tables within the workspace, such as the Customer table. However, this role does not automatically grant read access to tables that they do not need, like the Employee table.
E. Migrate the Employee table from Lakehouse1 to Lakehouse2: Moving the Employee table to a different lakehouse ensures that the data engineer does not have access to this sensitive table while still allowing them to interact with the Customer table. By separating the tables, you reduce the risk of inadvertently exposing PII.

By implementing these actions, you achieve the required access control and data separation, allowing the data engineer to work with the necessary data without accessing sensitive information.

Question No 8:

You have a Fabric warehouse named DW1. DW1 contains a table that stores sales data and is used by multiple sales representatives. You plan to implement row-level security (RLS). You need to ensure that the sales representatives can see only their respective data.

Which warehouse object do you require to implement RLS?

A. STORED PROCEDURE
B. CONSTRAINT
C. SCHEMA
D. FUNCTION

Answer: D. FUNCTION

Explanation:

To implement row-level security (RLS) in a warehouse, a function is required to define the security logic for filtering data based on user-specific criteria. RLS allows you to control access to rows in a table based on the identity of the user executing a query. The function defines how the data should be filtered according to the user's role or other identifying attributes, such as their sales territory or region.

While stored procedures (Option A) can be used for automation or procedural tasks, they do not directly facilitate row-level security. Constraints (Option B) are used to enforce rules on table columns, but they do not provide dynamic filtering based on user context, which is necessary for RLS. A schema (Option C) defines the structure and organization of database objects but does not handle security at the row level.

In RLS, a function is used to define the logic that applies filters to the rows in a table. For example, it could filter data based on the sales representative's ID or region, ensuring that each user only sees their data. Therefore, a function is the key warehouse object required for implementing RLS.