Microsoft DP-600 Exam Practice Questions, practice question pack
MICROSOFT · DP-600

Microsoft DP-600 Exam Practice Questions

222 questionsPDF by emailUpdated September 2026

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Three of the 222 questions in this pack

Question 1

CASE STUDY This is a case study. Case studies are not timed separately. You can use as much exam time as you would like to complete each case. However, there may be additional case studies and sections on this exam. You must manage your time to ensure that you are able to complete all questions included on this exam in the time provided. To answer the questions included in a case study, you will need to reference information that is provided in the case study. Case studies might contain exhibits and other resources that provide more information about the scenario that is described in the case study. Each question is independent of the other questions in this case study. At the end of this case study, a review screen will appear. This screen allows you to review your answers and to make changes before you move to the next section of the exam. After you begin a new section, you cannot return to this section. To start the case study -To display the first question in this case study, click the Next button. Use the buttons in the left pane to explore the content of the case study before you answer the questions. Clicking these buttons displays information such as business requirements, existing environment, and problem statements. If the case study has an All Information tab, note that the information displayed is identical to the information displayed on the subsequent tabs. When you are ready to answer a question, click the Question button to return to the question. Overview Contoso, Ltd. is a US-based health supplements company. Contoso has two divisions named Sales and Research. The Sales division contains two departments named Online Sales and Retail Sales. The Research division assigns internally developed product lines to individual teams of researchers and analysts. Existing Environment Identity Environment Contoso has a Microsoft Entra tenant named contoso.com. The tenant contains two groups named ResearchReviewersGroup1 and ResearchReviewersGroup2. Data Environment Contoso has the following data environment: The Sales division uses a Microsoft Power BI Premium capacity. The semantic model of the Online Sales department includes a fact table named Orders that uses Import made. In the system of origin, the OrderID value represents the sequence in which orders are created. The Research department uses an on-premises, third-party data warehousing product. Fabric is enabled for contoso.com. An Azure Data Lake Storage Gen2 storage account named storage1 contains Research division data for a product line named Productline1. The data is in the delta format. A Data Lake Storage Gen2 storage account named storage2 contains Research division data for a product line named Productline2. The data is in the CSV format. Requirements Planned Changes Contoso plans to make the following changes: Enable support for Fabric in the Power BI Premium capacity used by the Sales division. Make all the data for the Sales division and the Research division available in Fabric. For the Research division, create two Fabric workspaces named Productline1ws and Productine2ws. In Productline1ws, create a lakehouse named Lakehouse1. In Lakehouse1, create a shortcut to storage1 named ResearchProduct. Data Analytics Requirements Contoso identifies the following data analytics requirements: All the workspaces for the Sales division and the Research division must support all Fabric experiences. The Research division workspaces must use a dedicated, on-demand capacity that has per-minute billing. The Research division workspaces must be grouped together logically to support OneLake data hub filtering based on the department name. For the Research division workspaces, the members of ResearchReviewersGroup1 must be able to read lakehouse and warehouse data and shortcuts by using SQL endpoints. For the Research division workspaces, the members of ResearchReviewersGroup2 must be able to read lakehouse data by using Lakehouse explorer. All the semantic models and reports for the Research division must use version control that supports branching. Data Preparation Requirements Contoso identifies the following data preparation requirements: The Research division data for Productline1 must be retrieved from Lakehouse1 by using Fabric notebooks. All the Research division data in the lakehouses must be presented as managed tables in Lakehouse explorer. Semantic Model Requirements Contoso identifies the following requirements for implementing and managing semantic models: The number of rows added to the Orders table during refreshes must be minimized. The semantic models in the Research division workspaces must use Direct Lake mode. General Requirements Contoso identifies the following high-level requirements that must be considered for all solutions: Follow the principle of least privilege when applicable. Minimize implementation and maintenance effort when possible. You need to ensure that Contoso can use version control to meet the data analytics requirements and the general requirements. What should you do?

  1. Store at the semantic models and reports in Data Lake Gen2 storage.
  2. Modify the settings of the Research workspaces to use a GitHub repository.
  3. Modify the settings of the Research division workspaces to use an Azure Repos repository.
  4. Store all the semantic models and reports in Microsoft OneDrive.
Show answer and explanation

Correct answer: C. Modify the settings of the Research division workspaces to use an Azure Repos repository.

workspaces to use an Azure Repos repository. For Fabric workspaces, Azure Repos is the native version control solution that integrates directly with Fabric for managing semantic models and reports with branching support. Azure Repos provides enterprise-grade version control within the Microsoft ecosystem, minimizing implementation effort and maintaining security compliance. This satisfies the requirement for version control supporting branching while following least privilege principles through role-based access control in Azure Repos.

Why the other options are wrong

  • A. Storing files in Data Lake Gen2 storage does not provide version control capabilities or branching support.
  • B. While GitHub supports branching, Azure Repos is the Microsoft-native solution designed for Fabric integration and reduces maintenance overhead.
  • D. OneDrive does not provide version control with branching capabilities required for semantic model management.

Question 2

CASE STUDY This is a case study. Case studies are not timed separately. You can use as much exam time as you would like to complete each case. However, there may be additional case studies and sections on this exam. You must manage your time to ensure that you are able to complete all questions included on this exam in the time provided. To answer the questions included in a case study, you will need to reference information that is provided in the case study. Case studies might contain exhibits and other resources that provide more information about the scenario that is described in the case study. Each question is independent of the other questions in this case study. At the end of this case study, a review screen will appear. This screen allows you to review your answers and to make changes before you move to the next section of the exam. After you begin a new section, you cannot return to this section. To start the case study -To display the first question in this case study, click the Next button. Use the buttons in the left pane to explore the content of the case study before you answer the questions. Clicking these buttons displays information such as business requirements, existing environment, and problem statements. If the case study has an All Information tab, note that the information displayed is identical to the information displayed on the subsequent tabs. When you are ready to answer a question, click the Question button to return to the question. Overview Contoso, Ltd. is a US-based health supplements company. Contoso has two divisions named Sales and Research. The Sales division contains two departments named Online Sales and Retail Sales. The Research division assigns internally developed product lines to individual teams of researchers and analysts. Existing Environment Identity Environment Contoso has a Microsoft Entra tenant named contoso.com. The tenant contains two groups named ResearchReviewersGroup1 and ResearchReviewersGroup2. Data Environment Contoso has the following data environment: The Sales division uses a Microsoft Power BI Premium capacity. The semantic model of the Online Sales department includes a fact table named Orders that uses Import made. In the system of origin, the OrderID value represents the sequence in which orders are created. The Research department uses an on-premises, third-party data warehousing product. Fabric is enabled for contoso.com. An Azure Data Lake Storage Gen2 storage account named storage1 contains Research division data for a product line named Productline1. The data is in the delta format. A Data Lake Storage Gen2 storage account named storage2 contains Research division data for a product line named Productline2. The data is in the CSV format. Requirements Planned Changes Contoso plans to make the following changes: Enable support for Fabric in the Power BI Premium capacity used by the Sales division. Make all the data for the Sales division and the Research division available in Fabric. For the Research division, create two Fabric workspaces named Productline1ws and Productine2ws. In Productline1ws, create a lakehouse named Lakehouse1. In Lakehouse1, create a shortcut to storage1 named ResearchProduct. Data Analytics Requirements Contoso identifies the following data analytics requirements: All the workspaces for the Sales division and the Research division must support all Fabric experiences. The Research division workspaces must use a dedicated, on-demand capacity that has per-minute billing. The Research division workspaces must be grouped together logically to support OneLake data hub filtering based on the department name. For the Research division workspaces, the members of ResearchReviewersGroup1 must be able to read lakehouse and warehouse data and shortcuts by using SQL endpoints. For the Research division workspaces, the members of ResearchReviewersGroup2 must be able to read lakehouse data by using Lakehouse explorer. All the semantic models and reports for the Research division must use version control that supports branching. Data Preparation Requirements Contoso identifies the following data preparation requirements: The Research division data for Productline1 must be retrieved from Lakehouse1 by using Fabric notebooks. All the Research division data in the lakehouses must be presented as managed tables in Lakehouse explorer. Semantic Model Requirements Contoso identifies the following requirements for implementing and managing semantic models: The number of rows added to the Orders table during refreshes must be minimized. The semantic models in the Research division workspaces must use Direct Lake mode. General Requirements Contoso identifies the following high-level requirements that must be considered for all solutions: Follow the principle of least privilege when applicable. Minimize implementation and maintenance effort when possible. You need to refresh the Orders table of the Online Sales department. The solution must meet the semantic model requirements. What should you include in the solution?

  1. an Azure Data Factory pipeline that executes a Stored procedure activity to retrieve the maximum value of the OrderID column in the destination lakehouse
  2. an Azure Data Factory pipeline that executes a Stored procedure activity to retrieve the minimum value of the OrderID column in the destination lakehouse
  3. an Azure Data Factory pipeline that executes a dataflow to retrieve the minimum value of the OrderID column in the destination lakehouse
  4. an Azure Data Factory pipeline that executes a dataflow to retrieve the maximum value of the OrderID column in the destination lakehouse
Show answer and explanation

Correct answer: A. an Azure Data Factory pipeline that executes a Stored procedure activity to retrieve the maximum value of the OrderID column in the destination lakehouse

procedure activity to retrieve the maximum value of the OrderID column in the destination lakehouse The Orders table uses Import mode and OrderID reflects the sequence in which orders are created, so the refresh should load only the orders created after the last load. The pipeline first retrieves the maximum OrderID already present in the destination, then copies only rows with a higher OrderID, which minimizes the number of rows added during each refresh. A Stored procedure activity is the component that returns that watermark value to the pipeline so the copy can be filtered.

Why the other options are wrong

  • B. Starting from the minimum OrderID would reload rows that are already in the destination instead of adding only the new orders.
  • C. Retrieving the minimum OrderID reloads history that is already loaded, so the number of rows added is not minimized.
  • D. A dataflow is designed to move and transform data, not to return the watermark value that the pipeline needs to drive the incremental load.

Question 3

CASE STUDY This is a case study. Case studies are not timed separately. You can use as much exam time as you would like to complete each case. However, there may be additional case studies and sections on this exam. You must manage your time to ensure that you are able to complete all questions included on this exam in the time provided. To answer the questions included in a case study, you will need to reference information that is provided in the case study. Case studies might contain exhibits and other resources that provide more information about the scenario that is described in the case study. Each question is independent of the other questions in this case study. At the end of this case study, a review screen will appear. This screen allows you to review your answers and to make changes before you move to the next section of the exam. After you begin a new section, you cannot return to this section. To start the case study -To display the first question in this case study, click the Next button. Use the buttons in the left pane to explore the content of the case study before you answer the questions. Clicking these buttons displays information such as business requirements, existing environment, and problem statements. If the case study has an All Information tab, note that the information displayed is identical to the information displayed on the subsequent tabs. When you are ready to answer a question, click the Question button to return to the question. Overview Contoso, Ltd. is a US-based health supplements company. Contoso has two divisions named Sales and Research. The Sales division contains two departments named Online Sales and Retail Sales. The Research division assigns internally developed product lines to individual teams of researchers and analysts. Existing Environment Identity Environment Contoso has a Microsoft Entra tenant named contoso.com. The tenant contains two groups named ResearchReviewersGroup1 and ResearchReviewersGroup2. Data Environment Contoso has the following data environment: The Sales division uses a Microsoft Power BI Premium capacity. The semantic model of the Online Sales department includes a fact table named Orders that uses Import made. In the system of origin, the OrderID value represents the sequence in which orders are created. The Research department uses an on-premises, third-party data warehousing product. Fabric is enabled for contoso.com. An Azure Data Lake Storage Gen2 storage account named storage1 contains Research division data for a product line named Productline1. The data is in the delta format. A Data Lake Storage Gen2 storage account named storage2 contains Research division data for a product line named Productline2. The data is in the CSV format. Requirements Planned Changes Contoso plans to make the following changes: Enable support for Fabric in the Power BI Premium capacity used by the Sales division. Make all the data for the Sales division and the Research division available in Fabric. For the Research division, create two Fabric workspaces named Productline1ws and Productine2ws. In Productline1ws, create a lakehouse named Lakehouse1. In Lakehouse1, create a shortcut to storage1 named ResearchProduct. Data Analytics Requirements Contoso identifies the following data analytics requirements: All the workspaces for the Sales division and the Research division must support all Fabric experiences. The Research division workspaces must use a dedicated, on-demand capacity that has per-minute billing. The Research division workspaces must be grouped together logically to support OneLake data hub filtering based on the department name. For the Research division workspaces, the members of ResearchReviewersGroup1 must be able to read lakehouse and warehouse data and shortcuts by using SQL endpoints. For the Research division workspaces, the members of ResearchReviewersGroup2 must be able to read lakehouse data by using Lakehouse explorer. All the semantic models and reports for the Research division must use version control that supports branching. Data Preparation Requirements Contoso identifies the following data preparation requirements: The Research division data for Productline1 must be retrieved from Lakehouse1 by using Fabric notebooks. All the Research division data in the lakehouses must be presented as managed tables in Lakehouse explorer. Semantic Model Requirements Contoso identifies the following requirements for implementing and managing semantic models: The number of rows added to the Orders table during refreshes must be minimized. The semantic models in the Research division workspaces must use Direct Lake mode. General Requirements Contoso identifies the following high-level requirements that must be considered for all solutions: Follow the principle of least privilege when applicable. Minimize implementation and maintenance effort when possible. Which syntax should you use in a notebook to access the Research division data for Productline1?

  1. spark.read.format(“delta”).load(“Tables/productline1/ResearchProduct”)
  2. spark.sql(“SELECT * FROM Lakehouse1.ResearchProduct ”)
  3. external_table(‘Tables/ResearchProduct)
  4. external_table(ResearchProduct)
Show answer and explanation

Correct answer: B. spark.sql(“SELECT * FROM Lakehouse1.ResearchProduct ”)

Lakehouse1.ResearchProduct ”) To access data from Lakehouse1 using a shortcut named ResearchProduct in a Fabric notebook, the correct syntax is `spark.sql("SELECT * FROM Lakehouse1.ResearchProduct")`. This uses Spark SQL to query the lakehouse object directly by referencing the lakehouse name and the shortcut alias, which is the standard approach for notebook data retrieval in Fabric.

Why the other options are wrong

  • A. This syntax references an internal Tables folder path structure that is not the correct method for accessing shortcut data in notebooks.
  • C. The external_table function with this syntax is not valid Fabric notebook syntax for accessing lakehouse shortcuts.
  • D. This external_table syntax lacks the required string quotes and is not a valid Fabric function for data access.

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222 practice questions for Microsoft Fabric Analytics Engineer (DP-600), with full explanations.

Every question comes with the correct answer, the reasoning behind it, and a short note on why each wrong option is wrong. Work through it once with the answers, then again with the questions-only copy under exam conditions.

  • 222 questions mapped to the DP-600 exam objectives
  • Answers and explanations for every question, including the wrong options
  • A questions-only PDF for timed practice runs
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A DP-600 attempt costs US$165 in the US. This pack is US$39, paid once.

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Last updated September 2026 · 222 questions

What makes the DP-600 hard

The DP-600 sits at the intersection of Power BI and Azure data engineering, and that is exactly what catches people out. Microsoft Fabric combines data engineering, warehousing, real-time analytics and BI into one platform, and the DP-600 certifies the people building on top of it: when to use a lakehouse versus a warehouse, how Direct Lake mode falls back to DirectQuery, how DAX behaves under filter context, and how to query event data with KQL. Candidates who know Power BI well consistently underestimate the data engineering side, and that is where most failures come from.

Microsoft restructured the blueprint on 21 July 2026 into three domains, and preparing data is now the dominant one at 45 to 50%, close to half the exam. A study plan still built on the old four-domain split is spending time in the wrong place.

The exam runs to 40 to 60 scenario-based questions in 100 minutes, with a pass mark of 700 out of 1000. Microsoft does not test definitions, it tests decisions: the architecture scenarios, pipeline questions and semantic model framing that make up the bulk of the paper.

About the exam

The DP-600 certifies Fabric Analytics Engineers who design, implement and manage analytics solutions using Microsoft Fabric. It bridges data engineering and BI, requiring hands-on SQL, DAX and data modelling skills. No formal prerequisites. Skills measured as of 21 July 2026. Renews annually via a free Microsoft Learn assessment.

Exam domains

  • Maintain a data analytics solution: 25 to 30%
  • Prepare data: 45 to 50%
  • Implement and manage semantic models: 25 to 30%

40 to 60 questions, 100 minutes, passing score 700 out of 1000, US$165 in the US, priced by local currency elsewhere, online proctored and test centres, renews annually.

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222 practice questions as a PDF, each with the correct answer, a full explanation and a note on why the other options are wrong, plus a separate questions-only PDF for timed practice.

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