DP-600 Practice Questions

You are creating a PySpark notebook to clean a dataframe named df_sales. You need to remove rows where the CustomerID is null and drop any duplicate rows based on the TransactionID column.

Which code snippet should you use?

You are managing a Power BI semantic model that contains a calculation group named 'Time Intelligence'. You have an explicit measure named Total Sales. You want to prevent the calculation group from applying to a specific measure named Inventory Count because it is a snapshot metric and should not be summed over time.

What property should you configure in the Calculation Item DAX expression?

You are building a Star Schema in a Fabric Lakehouse. You have a FactSales table and a DimProduct table. You need to handle a Slowly Changing Dimension (SCD) Type 2 scenario for DimProduct using a PySpark notebook. The source system provides a full extract daily.

Which Delta Lake feature facilitates merging the new data while maintaining history?

Which TWO items are required to successfully configure a deployment pipeline in Microsoft Fabric to deploy a Lakehouse and its associated Notebooks from Development to Test? (Select TWO)

You are creating a Power BI report that uses a Direct Lake semantic model. You want to ensure that the report visuals render as fast as possible. You notice that some columns in your fact table have very high cardinality (e.g., a unique Transaction ID UUID).

How does high cardinality affect Direct Lake performance, and what should you do?

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?

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?

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?