Microsoft AZ-305 Data Integration and Analytics Architecture Practice Test

 

Topic 12 focuses on Data Integration and Analytics Architecture for the Microsoft Certified: Azure Solutions Architect Expert certification and the AZ-305 exam, using Microsoft Azure solution-architecture scenarios. For broader exam preparation, review the Microsoft Azure Solutions Architect Expert AZ-305 Exam Dumps page. Each question includes a concise explanation of the correct answer and the technical reason the other choices are incorrect.

Question 1

Which Azure capability provides scalable lake storage with hierarchical namespace for analytics engines and data integration workflows?

1. Azure Data Lake Storage Gen2
2. Azure Stream Analytics
3. Azure Data Explorer
4. Azure Databricks

Correct Answer: A

 

Correct Answer

Answer A is correct because Azure Data Lake Storage Gen2 matches the described capability and is intended to land large volumes of raw and curated analytics data.

Incorrect Answers

Answer B is incorrect because Azure Stream Analytics is intended to perform managed real-time stream processing and event analysis, which is a different architectural function.

Answer C is incorrect because Azure Data Explorer is intended to analyze large volumes of operational or time-series event data interactively, which is a different architectural function.

Answer D is incorrect because Azure Databricks is intended to perform large-scale distributed data engineering and advanced analytics, which is a different architectural function.

 

Question 2

When considering Azure Databricks, which requirement supports that choice?

1. To collect large event streams for analytics pipelines.
2. To support integrated enterprise analytics and data warehousing scenarios.
3. To perform large-scale distributed data engineering and advanced analytics.
4. To serve governed semantic models to business intelligence tools.

Correct Answer: C

 

Correct Answer

Answer C is correct because Azure Databricks provides an Apache Spark-based analytics platform for engineering, machine learning, and lakehouse workloads.

Incorrect Answers

Answer A is incorrect because that outcome is more directly associated with Azure Event Hubs, not Azure Databricks.

Answer B is incorrect because that outcome is more directly associated with Azure Synapse Analytics, not Azure Databricks.

Answer D is incorrect because that outcome is more directly associated with Azure Analysis Services, not Azure Databricks.

 

Question 3

An analytics platform separates its persistent data lake from its processing engines. Several engines must access the same files through a hierarchical namespace. Which statement describes the role of Azure Data Lake Storage Gen2 in this architecture?

1. Processes streaming data in near real time with SQL-like queries and supported event inputs and outputs.
2. Provides an Apache Spark-based analytics platform for engineering, machine learning, and lakehouse workloads.
3. Provides scalable lake storage with hierarchical namespace for analytics engines and data integration workflows.
4. Provides fast exploration and analytics over high-volume telemetry, logs, and time-series data using Kusto Query Language.

Correct Answer: C

 

Correct Answer

Answer C is correct because Azure Data Lake Storage Gen2 supplies persistent, hierarchical file storage that multiple analytics engines can use. The processing engines remain separate from the shared lake storage.

Incorrect Answers

Answer A is incorrect because that description belongs to Azure Stream Analytics, whose purpose is to perform managed real-time stream processing and event analysis.

Answer B is incorrect because that description belongs to Azure Databricks, whose purpose is to perform large-scale distributed data engineering and advanced analytics.

Answer D is incorrect because that description belongs to Azure Data Explorer, whose purpose is to analyze large volumes of operational or time-series event data interactively.

 

Question 4

To serve governed semantic models to business intelligence tools, which Azure design option should be selected?

1. Data Factory in Microsoft Fabric
2. Azure Analysis Services
3. Azure Databricks
4. Microsoft Fabric OneLake

Correct Answer: B

 

Correct Answer

Answer B is correct because Azure Analysis Services is designed to serve governed semantic models to business intelligence tools. Azure Analysis Services provides managed semantic models for enterprise BI scenarios that require Analysis Services tabular models.

Incorrect Answers

Answer A is incorrect because Data Factory in Microsoft Fabric can be useful in Azure architectures, but its primary role is to design new Fabric-centric data integration workflows when an integrated analytics platform is preferred; it is not the best match for the stated priority.

Answer C is incorrect because Azure Databricks can be useful in Azure architectures, but its primary role is to perform large-scale distributed data engineering and advanced analytics; it is not the best match for the stated priority.

Answer D is incorrect because Microsoft Fabric OneLake can be useful in Azure architectures, but its primary role is to centralize Fabric analytics data under one lake-centric architecture; it is not the best match for the stated priority.

 

Question 5

To perform large-scale distributed data engineering and advanced analytics, which Azure design option should be selected?

1. Azure Databricks
2. Azure Synapse Analytics
3. Azure Analysis Services
4. Azure Event Hubs

Correct Answer: A

 

Correct Answer

Answer A is correct because Azure Databricks is designed to perform large-scale distributed data engineering and advanced analytics. Azure Databricks provides an Apache Spark-based analytics platform for engineering, machine learning, and lakehouse workloads.

Incorrect Answers

Answer B is incorrect because Azure Synapse Analytics can be useful in Azure architectures, but its primary role is to support integrated enterprise analytics and data warehousing scenarios; it is not the best match for the stated priority.

Answer C is incorrect because Azure Analysis Services can be useful in Azure architectures, but its primary role is to serve governed semantic models to business intelligence tools; it is not the best match for the stated priority.

Answer D is incorrect because Azure Event Hubs can be useful in Azure architectures, but its primary role is to collect large event streams for analytics pipelines; it is not the best match for the stated priority.

 

Question 6

When considering Azure Data Lake Storage Gen2, which requirement supports that choice?

1. To analyze large volumes of operational or time-series event data interactively.
2. To land large volumes of raw and curated analytics data.
3. To perform managed real-time stream processing and event analysis.
4. To perform large-scale distributed data engineering and advanced analytics.

Correct Answer: B

 

Correct Answer

Answer B is correct because Azure Data Lake Storage Gen2 provides scalable lake storage with hierarchical namespace for analytics engines and data integration workflows.

Incorrect Answers

Answer A is incorrect because that outcome is more directly associated with Azure Data Explorer, not Azure Data Lake Storage Gen2.

Answer C is incorrect because that outcome is more directly associated with Azure Stream Analytics, not Azure Data Lake Storage Gen2.

Answer D is incorrect because that outcome is more directly associated with Azure Databricks, not Azure Data Lake Storage Gen2.

 

Question 7

A telemetry platform must support interactive KQL analysis across very large volumes of operational and time-series events. Which design objective most directly fits Azure Data Explorer?

1. To land large volumes of raw and curated analytics data.
2. To build managed batch-oriented data integration and transformation workflows.
3. To serve governed semantic models to business intelligence tools.
4. To analyze large volumes of operational or time-series event data interactively.

Correct Answer: D

 

Correct Answer

Answer D is correct because Azure Data Explorer provides fast exploration and analytics over high-volume telemetry, logs, and time-series data using Kusto Query Language.

Incorrect Answers

Answer A is incorrect because that outcome is more directly associated with Azure Data Lake Storage Gen2, not Azure Data Explorer.

Answer B is incorrect because that outcome is more directly associated with Azure Data Factory, not Azure Data Explorer.

Answer C is incorrect because that outcome is more directly associated with Azure Analysis Services, not Azure Data Explorer.

 

Question 8

Which Azure capability provides managed semantic models for enterprise BI scenarios that require Analysis Services tabular models?

1. Azure Analysis Services
2. Data Factory in Microsoft Fabric
3. Microsoft Fabric OneLake
4. Azure Databricks

Correct Answer: A

 

Correct Answer

Answer A is correct because Azure Analysis Services matches the described capability and is intended to serve governed semantic models to business intelligence tools.

Incorrect Answers

Answer B is incorrect because Data Factory in Microsoft Fabric is intended to design new Fabric-centric data integration workflows when an integrated analytics platform is preferred, which is a different architectural function.

Answer C is incorrect because Microsoft Fabric OneLake is intended to centralize Fabric analytics data under one lake-centric architecture, which is a different architectural function.

Answer D is incorrect because Azure Databricks is intended to perform large-scale distributed data engineering and advanced analytics, which is a different architectural function.

 

Question 9

To perform managed real-time stream processing and event analysis, which Azure design option should be selected?

1. Azure Data Factory
2. Azure Stream Analytics
3. Azure Analysis Services
4. Azure Event Hubs

Correct Answer: B

 

Correct Answer

Answer B is correct because Azure Stream Analytics is designed to perform managed real-time stream processing and event analysis. Azure Stream Analytics processes streaming data in near real time with SQL-like queries and supported event inputs and outputs.

Incorrect Answers

Answer A is incorrect because Azure Data Factory can be useful in Azure architectures, but its primary role is to build managed batch-oriented data integration and transformation workflows; it is not the best match for the stated priority.

Answer C is incorrect because Azure Analysis Services can be useful in Azure architectures, but its primary role is to serve governed semantic models to business intelligence tools; it is not the best match for the stated priority.

Answer D is incorrect because Azure Event Hubs can be useful in Azure architectures, but its primary role is to collect large event streams for analytics pipelines; it is not the best match for the stated priority.

 

Question 10

When considering Azure Data Factory, which requirement supports that choice?

1. To perform managed real-time stream processing and event analysis.
2. To perform large-scale distributed data engineering and advanced analytics.
3. To build managed batch-oriented data integration and transformation workflows.
4. To design new Fabric-centric data integration workflows when an integrated analytics platform is preferred.

Correct Answer: C

 

Correct Answer

Answer C is correct because Azure Data Factory orchestrates and moves data through pipelines and supports broad connectivity across cloud and on-premises sources.

Incorrect Answers

Answer A is incorrect because that outcome is more directly associated with Azure Stream Analytics, not Azure Data Factory.

Answer B is incorrect because that outcome is more directly associated with Azure Databricks, not Azure Data Factory.

Answer D is incorrect because that outcome is more directly associated with Data Factory in Microsoft Fabric, not Azure Data Factory.

 

Question 11

Which Azure capability provides fast exploration and analytics over high-volume telemetry, logs, and time-series data using Kusto Query Language?

1. Azure Analysis Services
2. Azure Data Lake Storage Gen2
3. Azure Data Factory
4. Azure Data Explorer

Correct Answer: D

 

Correct Answer

Answer D is correct because Azure Data Explorer matches the described capability and is intended to analyze large volumes of operational or time-series event data interactively.

Incorrect Answers

Answer A is incorrect because Azure Analysis Services is intended to serve governed semantic models to business intelligence tools, which is a different architectural function.

Answer B is incorrect because Azure Data Lake Storage Gen2 is intended to land large volumes of raw and curated analytics data, which is a different architectural function.

Answer C is incorrect because Azure Data Factory is intended to build managed batch-oriented data integration and transformation workflows, which is a different architectural function.

 

Question 12

To collect large event streams for analytics pipelines, which Azure design option should be selected?

1. Azure Event Hubs
2. Data Factory in Microsoft Fabric
3. Microsoft Fabric OneLake
4. Azure Data Explorer

Correct Answer: A

 

Correct Answer

Answer A is correct because Azure Event Hubs is designed to collect large event streams for analytics pipelines. Azure Event Hubs ingests high-volume event streams such as telemetry, logs, or application events for downstream processing.

Incorrect Answers

Answer B is incorrect because Data Factory in Microsoft Fabric can be useful in Azure architectures, but its primary role is to design new Fabric-centric data integration workflows when an integrated analytics platform is preferred; it is not the best match for the stated priority.

Answer C is incorrect because Microsoft Fabric OneLake can be useful in Azure architectures, but its primary role is to centralize Fabric analytics data under one lake-centric architecture; it is not the best match for the stated priority.

Answer D is incorrect because Azure Data Explorer can be useful in Azure architectures, but its primary role is to analyze large volumes of operational or time-series event data interactively; it is not the best match for the stated priority.

 

Question 13

To design new Fabric-centric data integration workflows when an integrated analytics platform is preferred, which Azure design option should be selected?

1. Azure Synapse Analytics
2. Azure Event Hubs
3. Data Factory in Microsoft Fabric
4. Azure Data Lake Storage Gen2

Correct Answer: C

 

Correct Answer

Answer C is correct because Data Factory in Microsoft Fabric is designed to design new Fabric-centric data integration workflows when an integrated analytics platform is preferred. Data Factory in Microsoft Fabric is the newer Fabric-based data integration experience that provides pipelines and dataflow capabilities within Microsoft Fabric.

Incorrect Answers

Answer A is incorrect because Azure Synapse Analytics can be useful in Azure architectures, but its primary role is to support integrated enterprise analytics and data warehousing scenarios; it is not the best match for the stated priority.

Answer B is incorrect because Azure Event Hubs can be useful in Azure architectures, but its primary role is to collect large event streams for analytics pipelines; it is not the best match for the stated priority.

Answer D is incorrect because Azure Data Lake Storage Gen2 can be useful in Azure architectures, but its primary role is to land large volumes of raw and curated analytics data; it is not the best match for the stated priority.

 

Question 14

Which Azure capability provides an Apache Spark-based analytics platform for engineering, machine learning, and lakehouse workloads?

1. Azure Synapse Analytics
2. Azure Databricks
3. Azure Event Hubs
4. Azure Analysis Services

Correct Answer: B

 

Correct Answer

Answer B is correct because Azure Databricks matches the described capability and is intended to perform large-scale distributed data engineering and advanced analytics.

Incorrect Answers

Answer A is incorrect because Azure Synapse Analytics is intended to support integrated enterprise analytics and data warehousing scenarios, which is a different architectural function.

Answer C is incorrect because Azure Event Hubs is intended to collect large event streams for analytics pipelines, which is a different architectural function.

Answer D is incorrect because Azure Analysis Services is intended to serve governed semantic models to business intelligence tools, which is a different architectural function.

 

Question 15

For Azure Analysis Services, which statement is accurate?

1. Provides an Apache Spark-based analytics platform for engineering, machine learning, and lakehouse workloads.
2. Provides a unified logical data lake for Microsoft Fabric workloads.
3. Is the newer Fabric-based data integration experience that provides pipelines and dataflow capabilities within Microsoft Fabric.
4. Provides managed semantic models for enterprise BI scenarios that require Analysis Services tabular models.

Correct Answer: D

 

Correct Answer

Answer D is correct because Azure Analysis Services provides managed semantic models for enterprise BI scenarios that require Analysis Services tabular models.

Incorrect Answers

Answer A is incorrect because that description belongs to Azure Databricks, whose purpose is to perform large-scale distributed data engineering and advanced analytics.

Answer B is incorrect because that description belongs to Microsoft Fabric OneLake, whose purpose is to centralize Fabric analytics data under one lake-centric architecture.

Answer C is incorrect because that description belongs to Data Factory in Microsoft Fabric, whose purpose is to design new Fabric-centric data integration workflows when an integrated analytics platform is preferred.

 

Question 16

To centralize Fabric analytics data under one lake-centric architecture, which Azure design option should be selected?

1. Microsoft Fabric OneLake
2. Azure Synapse Analytics
3. Azure Data Lake Storage Gen2
4. Azure Data Factory

Correct Answer: A

 

Correct Answer

Answer A is correct because Microsoft Fabric OneLake is designed to centralize Fabric analytics data under one lake-centric architecture. Microsoft Fabric OneLake provides a unified logical data lake for Microsoft Fabric workloads.

Incorrect Answers

Answer B is incorrect because Azure Synapse Analytics can be useful in Azure architectures, but its primary role is to support integrated enterprise analytics and data warehousing scenarios; it is not the best match for the stated priority.

Answer C is incorrect because Azure Data Lake Storage Gen2 can be useful in Azure architectures, but its primary role is to land large volumes of raw and curated analytics data; it is not the best match for the stated priority.

Answer D is incorrect because Azure Data Factory can be useful in Azure architectures, but its primary role is to build managed batch-oriented data integration and transformation workflows; it is not the best match for the stated priority.

 

Question 17

For Azure Data Factory, which statement is accurate?

1. Orchestrates and moves data through pipelines and supports broad connectivity across cloud and on-premises sources.
2. Is the newer Fabric-based data integration experience that provides pipelines and dataflow capabilities within Microsoft Fabric.
3. Processes streaming data in near real time with SQL-like queries and supported event inputs and outputs.
4. Provides an Apache Spark-based analytics platform for engineering, machine learning, and lakehouse workloads.

Correct Answer: A

 

Correct Answer

Answer A is correct because Azure Data Factory orchestrates and moves data through pipelines and supports broad connectivity across cloud and on-premises sources.

Incorrect Answers

Answer B is incorrect because that description belongs to Data Factory in Microsoft Fabric, whose purpose is to design new Fabric-centric data integration workflows when an integrated analytics platform is preferred.

Answer C is incorrect because that description belongs to Azure Stream Analytics, whose purpose is to perform managed real-time stream processing and event analysis.

Answer D is incorrect because that description belongs to Azure Databricks, whose purpose is to perform large-scale distributed data engineering and advanced analytics.

 

Question 18

When considering Data Factory in Microsoft Fabric, which requirement supports that choice?

1. To collect large event streams for analytics pipelines.
2. To land large volumes of raw and curated analytics data.
3. To design new Fabric-centric data integration workflows when an integrated analytics platform is preferred.
4. To support integrated enterprise analytics and data warehousing scenarios.

Correct Answer: C

 

Correct Answer

Answer C is correct because Data Factory in Microsoft Fabric is the newer Fabric-based data integration experience that provides pipelines and dataflow capabilities within Microsoft Fabric.

Incorrect Answers

Answer A is incorrect because that outcome is more directly associated with Azure Event Hubs, not Data Factory in Microsoft Fabric.

Answer B is incorrect because that outcome is more directly associated with Azure Data Lake Storage Gen2, not Data Factory in Microsoft Fabric.

Answer D is incorrect because that outcome is more directly associated with Azure Synapse Analytics, not Data Factory in Microsoft Fabric.

 

Question 19

A Microsoft Fabric environment needs one lake-centric foundation shared across its analytics workloads. Which Fabric data layer should the architect use?

1. Azure Synapse Analytics
2. Microsoft Fabric OneLake
3. Azure Data Lake Storage Gen2
4. Azure Data Factory

Correct Answer: B

 

Correct Answer

Answer B is correct because Microsoft Fabric OneLake provides a unified logical data lake for Microsoft Fabric workloads. It directly meets the requirement to centralize Fabric analytics data under one lake-centric architecture.

Incorrect Answers

Answer A is incorrect because Azure Synapse Analytics is used to support integrated enterprise analytics and data warehousing scenarios; that does not directly satisfy the requirement in this scenario.

Answer C is incorrect because Azure Data Lake Storage Gen2 is used to land large volumes of raw and curated analytics data; that does not directly satisfy the requirement in this scenario.

Answer D is incorrect because Azure Data Factory is used to build managed batch-oriented data integration and transformation workflows; that does not directly satisfy the requirement in this scenario.

 

Question 20

To support integrated enterprise analytics and data warehousing scenarios, which Azure design option should be selected?

1. Microsoft Fabric OneLake
2. Azure Synapse Analytics
3. Azure Data Explorer
4. Azure Stream Analytics

Correct Answer: B

 

Correct Answer

Answer B is correct because Azure Synapse Analytics is designed to support integrated enterprise analytics and data warehousing scenarios. Azure Synapse Analytics combines data warehousing, Spark, integration, and analytics capabilities in an Azure analytics workspace.

Incorrect Answers

Answer A is incorrect because Microsoft Fabric OneLake can be useful in Azure architectures, but its primary role is to centralize Fabric analytics data under one lake-centric architecture; it is not the best match for the stated priority.

Answer C is incorrect because Azure Data Explorer can be useful in Azure architectures, but its primary role is to analyze large volumes of operational or time-series event data interactively; it is not the best match for the stated priority.

Answer D is incorrect because Azure Stream Analytics can be useful in Azure architectures, but its primary role is to perform managed real-time stream processing and event analysis; it is not the best match for the stated priority.

 

Question 21

For Azure Stream Analytics, which statement is accurate?

1. Ingests high-volume event streams such as telemetry, logs, or application events for downstream processing.
2. Processes streaming data in near real time with SQL-like queries and supported event inputs and outputs.
3. Provides managed semantic models for enterprise BI scenarios that require Analysis Services tabular models.
4. Orchestrates and moves data through pipelines and supports broad connectivity across cloud and on-premises sources.

Correct Answer: B

 

Correct Answer

Answer B is correct because Azure Stream Analytics processes streaming data in near real time with SQL-like queries and supported event inputs and outputs.

Incorrect Answers

Answer A is incorrect because that description belongs to Azure Event Hubs, whose purpose is to collect large event streams for analytics pipelines.

Answer C is incorrect because that description belongs to Azure Analysis Services, whose purpose is to serve governed semantic models to business intelligence tools.

Answer D is incorrect because that description belongs to Azure Data Factory, whose purpose is to build managed batch-oriented data integration and transformation workflows.

 

Question 22

An enterprise wants an Azure platform that combines data warehousing with integrated analytics capabilities. Which listed service should the architect evaluate?

1. Azure Stream Analytics
2. Microsoft Fabric OneLake
3. Azure Data Explorer
4. Azure Synapse Analytics

Correct Answer: D

 

Correct Answer

Answer D is correct because Azure Synapse Analytics combines data warehousing, Spark, integration, and analytics capabilities in an Azure analytics workspace. It directly meets the requirement to support integrated enterprise analytics and data warehousing scenarios.

Incorrect Answers

Answer A is incorrect because Azure Stream Analytics is used to perform managed real-time stream processing and event analysis; that does not directly satisfy the requirement in this scenario.

Answer B is incorrect because Microsoft Fabric OneLake is used to centralize Fabric analytics data under one lake-centric architecture; that does not directly satisfy the requirement in this scenario.

Answer C is incorrect because Azure Data Explorer is used to analyze large volumes of operational or time-series event data interactively; that does not directly satisfy the requirement in this scenario.

 

Question 23

A data engineering team needs a managed Apache Spark platform for large-scale transformation, machine learning, and lakehouse workloads. Which description correctly identifies Azure Databricks?

1. Combines data warehousing, Spark, integration, and analytics capabilities in an Azure analytics workspace.
2. Provides managed semantic models for enterprise BI scenarios that require Analysis Services tabular models.
3. Ingests high-volume event streams such as telemetry, logs, or application events for downstream processing.
4. Provides an Apache Spark-based analytics platform for engineering, machine learning, and lakehouse workloads.

Correct Answer: D

 

Correct Answer

Answer D is correct because Azure Databricks provides an Apache Spark-based analytics platform for engineering, machine learning, and lakehouse workloads.

Incorrect Answers

Answer A is incorrect because that description belongs to Azure Synapse Analytics, whose purpose is to support integrated enterprise analytics and data warehousing scenarios.

Answer B is incorrect because that description belongs to Azure Analysis Services, whose purpose is to serve governed semantic models to business intelligence tools.

Answer C is incorrect because that description belongs to Azure Event Hubs, whose purpose is to collect large event streams for analytics pipelines.

 

Question 24

When considering Azure Synapse Analytics, which requirement supports that choice?

1. To perform managed real-time stream processing and event analysis.
2. To support integrated enterprise analytics and data warehousing scenarios.
3. To centralize Fabric analytics data under one lake-centric architecture.
4. To analyze large volumes of operational or time-series event data interactively.

Correct Answer: B

 

Correct Answer

Answer B is correct because Azure Synapse Analytics combines data warehousing, Spark, integration, and analytics capabilities in an Azure analytics workspace.

Incorrect Answers

Answer A is incorrect because that outcome is more directly associated with Azure Stream Analytics, not Azure Synapse Analytics.

Answer C is incorrect because that outcome is more directly associated with Microsoft Fabric OneLake, not Azure Synapse Analytics.

Answer D is incorrect because that outcome is more directly associated with Azure Data Explorer, not Azure Synapse Analytics.

 

Question 25

Which Azure capability provides a unified logical data lake for Microsoft Fabric workloads?

1. Azure Data Factory
2. Azure Synapse Analytics
3. Microsoft Fabric OneLake
4. Azure Data Lake Storage Gen2

Correct Answer: C

 

Correct Answer

Answer C is correct because Microsoft Fabric OneLake matches the described capability and is intended to centralize Fabric analytics data under one lake-centric architecture.

Incorrect Answers

Answer A is incorrect because Azure Data Factory is intended to build managed batch-oriented data integration and transformation workflows, which is a different architectural function.

Answer B is incorrect because Azure Synapse Analytics is intended to support integrated enterprise analytics and data warehousing scenarios, which is a different architectural function.

Answer D is incorrect because Azure Data Lake Storage Gen2 is intended to land large volumes of raw and curated analytics data, which is a different architectural function.

 

Question 26

An architect is selecting a service for fast exploratory queries over logs, telemetry, and time-series data using KQL. Which description correctly characterizes Azure Data Explorer?

1. Provides fast exploration and analytics over high-volume telemetry, logs, and time-series data using Kusto Query Language.
2. Provides managed semantic models for enterprise BI scenarios that require Analysis Services tabular models.
3. Provides scalable lake storage with hierarchical namespace for analytics engines and data integration workflows.
4. Orchestrates and moves data through pipelines and supports broad connectivity across cloud and on-premises sources.

Correct Answer: A

 

Correct Answer

Answer A is correct because Azure Data Explorer provides fast exploration and analytics over high-volume telemetry, logs, and time-series data using Kusto Query Language.

Incorrect Answers

Answer B is incorrect because that description belongs to Azure Analysis Services, whose purpose is to serve governed semantic models to business intelligence tools.

Answer C is incorrect because that description belongs to Azure Data Lake Storage Gen2, whose purpose is to land large volumes of raw and curated analytics data.

Answer D is incorrect because that description belongs to Azure Data Factory, whose purpose is to build managed batch-oriented data integration and transformation workflows.

 

Question 27

To analyze large volumes of operational or time-series event data interactively, which Azure design option should be selected?

1. Azure Data Factory
2. Azure Analysis Services
3. Azure Data Lake Storage Gen2
4. Azure Data Explorer

Correct Answer: D

 

Correct Answer

Answer D is correct because Azure Data Explorer is designed to analyze large volumes of operational or time-series event data interactively. Azure Data Explorer provides fast exploration and analytics over high-volume telemetry, logs, and time-series data using Kusto Query Language.

Incorrect Answers

Answer A is incorrect because Azure Data Factory can be useful in Azure architectures, but its primary role is to build managed batch-oriented data integration and transformation workflows; it is not the best match for the stated priority.

Answer B is incorrect because Azure Analysis Services can be useful in Azure architectures, but its primary role is to serve governed semantic models to business intelligence tools; it is not the best match for the stated priority.

Answer C is incorrect because Azure Data Lake Storage Gen2 can be useful in Azure architectures, but its primary role is to land large volumes of raw and curated analytics data; it is not the best match for the stated priority.

 

Question 28

For Microsoft Fabric OneLake, which statement is accurate?

1. Provides a unified logical data lake for Microsoft Fabric workloads.
2. Combines data warehousing, Spark, integration, and analytics capabilities in an Azure analytics workspace.
3. Orchestrates and moves data through pipelines and supports broad connectivity across cloud and on-premises sources.
4. Provides scalable lake storage with hierarchical namespace for analytics engines and data integration workflows.

Correct Answer: A

 

Correct Answer

Answer A is correct because Microsoft Fabric OneLake provides a unified logical data lake for Microsoft Fabric workloads.

Incorrect Answers

Answer B is incorrect because that description belongs to Azure Synapse Analytics, whose purpose is to support integrated enterprise analytics and data warehousing scenarios.

Answer C is incorrect because that description belongs to Azure Data Factory, whose purpose is to build managed batch-oriented data integration and transformation workflows.

Answer D is incorrect because that description belongs to Azure Data Lake Storage Gen2, whose purpose is to land large volumes of raw and curated analytics data.

 

Question 29

When considering Microsoft Fabric OneLake, which requirement supports that choice?

1. To build managed batch-oriented data integration and transformation workflows.
2. To land large volumes of raw and curated analytics data.
3. To centralize Fabric analytics data under one lake-centric architecture.
4. To support integrated enterprise analytics and data warehousing scenarios.

Correct Answer: C

 

Correct Answer

Answer C is correct because Microsoft Fabric OneLake provides a unified logical data lake for Microsoft Fabric workloads.

Incorrect Answers

Answer A is incorrect because that outcome is more directly associated with Azure Data Factory, not Microsoft Fabric OneLake.

Answer B is incorrect because that outcome is more directly associated with Azure Data Lake Storage Gen2, not Microsoft Fabric OneLake.

Answer D is incorrect because that outcome is more directly associated with Azure Synapse Analytics, not Microsoft Fabric OneLake.

 

Question 30

Which Azure capability is the newer Fabric-based data integration experience that provides pipelines and dataflow capabilities within Microsoft Fabric?

1. Azure Data Lake Storage Gen2
2. Azure Event Hubs
3. Azure Synapse Analytics
4. Data Factory in Microsoft Fabric

Correct Answer: D

 

Correct Answer

Answer D is correct because Data Factory in Microsoft Fabric matches the described capability and is intended to design new Fabric-centric data integration workflows when an integrated analytics platform is preferred.

Incorrect Answers

Answer A is incorrect because Azure Data Lake Storage Gen2 is intended to land large volumes of raw and curated analytics data, which is a different architectural function.

Answer B is incorrect because Azure Event Hubs is intended to collect large event streams for analytics pipelines, which is a different architectural function.

Answer C is incorrect because Azure Synapse Analytics is intended to support integrated enterprise analytics and data warehousing scenarios, which is a different architectural function.