DP-600 practice questions 91–100

1. Question 91 · 190 in bank

   Select 2

   You have a Fabric workspace that contains a DirectQuery semantic model. The model queries a data source that has 500 million rows. You have a Microsoft Power Bi report named Report1 that uses the model. Report1 contains visuals on multiple pages. You need to reduce the query execution time for the visuals on all the pages. What are two features that you can use? Each correct answer presents a complete solution, NOTE: Each correct answer is worth one point.

   • Auser-defined aggregations
   • Bautomatic aggregation
   • Cquery caching
   • DOneLake integration

   Show answer

   Answer: AB

   Explanation

   Agree with lengzhai's reference of the 2 links: A - Custom aggregations enables PBI to not perform a Full Scan of the underlying datasets. B - The AutoAggregations feature automatically creates aggregations on large datasets and based on query optimization determines the total number of rows that requires processing based on the generated query plan. Incorrect to this question context: C - Although caching helps improve performance on large datasets, it doesn't support DirectQuery (Important note in https://learn.microsoft.com/en-us/power-bi/connect-data/power-bi-query-caching) ; Also, it is a feature available in PBI Service that is automatic and needs no intervention from the user.

   Source: Adapted from community discussion — sraakesh95

   References

   • Microsoft Learn
   • Microsoft Learn
   • Microsoft Learn

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2. Question 92 · 190 in bank

   You have a Fabric tenant that contains 30 CSV files in OneLake. The files are updated daily. You create a Microsoft Power BI semantic model named Model1 that uses the CSV files as a data source. You configure incremental refresh for Model1 and publish the model to a Premium capacity in the Fabric tenant. When you initiate a refresh of Model1, the refresh fails after running out of resources. What is a possible cause of the failure?

   • AQuery folding is occurring.
   • BOnly refresh complete days is selected.
   • CXMLA Endpoint is set to Read Only.
   • DQuery folding is NOT occurring.
   • EThe delta type of the column used to partition the data has changed.

   Show answer

   Answer: D

   Explanation

   D. Query folding is NOT occurring. Query folding refers to the ability of Power Query to push data transformation logic back to the data source, which can perform the transformations more efficiently. When query folding does not occur, all the data is pulled into Power BI and transformations are applied locally, which can be resource-intensive and lead to running out of resources, especially with large datasets like your 30 CSV files. E. The delta type of the column used to partition the data has changed: While this could cause issues with incremental refresh accuracy, it would not typically result in "running out of resources" during the refresh.

   Source: Adapted from community discussion — 282b85d

   References

   • Microsoft Learn
   • Microsoft Learn
   • Microsoft Learn

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3. Question 93 · 190 in bank

   You have a Fabric tenant that uses a Microsoft Power BI Premium capacity. You need to enable scale-out for a semantic model. What should you do first?

   • AAt the semantic model level, set Large dataset storage format to Off.
   • BAt the tenant level, set Create and use Metrics to Enabled.
   • CAt the semantic model level, set Large dataset storage format to On.
   • DAt the tenant level, set Data Activator to Enabled.

   Show answer

   Answer: C

   Explanation

   To enable scale-out for a semantic model in Power BI Premium, the Large dataset storage format must first be turned On. This is because the large dataset storage format allows models to take advantage of premium capacity features, including scale-out capabilities, which help distribute the load across multiple replicas for high-concurrency scenarios.

   Source: Adapted from community discussion — Charley92

   References

   • Microsoft Learn
   • Microsoft Learn

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4. Question 94 · 190 in bank

   You have a Fabric tenant that contains a warehouse. The warehouse uses row-level security (RLS). You create a Direct Lake semantic model that uses the Delta tables and RLS of the warehouse. When users interact with a report built from the model, which mode will be used by the DAX queries?

   • ADirectQuery
   • BDual
   • CDirect Lake
   • DImport

   Show answer

   Answer: A

   Explanation

   A. Direct Query "Row-level security only applies to queries on a Warehouse or SQL analytics endpoint in Fabric. Power BI queries on a warehouse in Direct Lake mode will fall back to Direct Query mode to abide by row-level security." https://learn.microsoft.com/en-us/fabric/data-warehouse/row-level-security

   Source: Adapted from community discussion — wojciech_wie

   References

   • Microsoft Learn
   • Microsoft Learn
   • Microsoft Learn

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5. Question 95 · 190 in bank

   You have a Fabric tenant that contains a complex semantic model. The model is based on a star schema and contains many tables, including a fact table named Sales. You need to create a diagram of the model. The diagram must contain only the Sales table and related tables. What should you use from Microsoft Power BI Desktop?

   • Adata categories
   • BData view
   • CModel view
   • DDAX query view

   Show answer

   Answer: C

   References

   • Microsoft Learn

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6. Question 96 · 190 in bank

   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?

   • Aan Azure Data Factory pipeline that executes a Stored procedure activity to retrieve the maximum value of the OrderID column in the destination lakehouse
   • Ban Azure Data Factory pipeline that executes a Stored procedure activity to retrieve the minimum value of the OrderID column in the destination lakehouse
   • Can Azure Data Factory pipeline that executes a dataflow to retrieve the minimum value of the OrderID column in the destination lakehouse
   • Dan Azure Data Factory pipeline that executes a dataflow to retrieve the maximum value of the OrderID column in the destination lakehouse

   Show answer

   Answer: D

   Explanation

   we need to retrieve the maximum OrderID in the destination table to minimize the number of rows added during refresh. this would be an incremental load. can be done with data flows

   Source: Adapted from community discussion — theseon

   References

   • Microsoft Learn
   • Microsoft Learn
   • Microsoft Learn

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7. Question 97 · 190 in bank

   Select 2

   You have a Fabric tenant that contains a semantic model. The model uses Direct Lake mode. You suspect that some DAX queries load unnecessary columns into memory. You need to identify the frequently used columns that are loaded into memory. What are two ways to achieve the goal? Each correct answer presents a complete solution. NOTE: Each correct answer is worth one point.

   • AUse the Analyze in Excel feature.
   • BUse the Vertipaq Analyzer tool.
   • CQuery the $System.DISCOVER_STORAGE_TABLE_COLUMN_SEGMENTS dynamic management view (DMV).
   • DQuery the DISCOVER_MEMORYGRANT dynamic management view (DMV).

   Show answer

   Answer: BC

   Explanation

   Methods to Identify Frequently Used Columns: B. Use the Vertipaq Analyzer tool. Vertipaq Analyzer: This tool helps analyze the internal structure of your Power BI model. It provides detailed information about the storage and memory usage of your model, including which columns are frequently accessed and loaded into memory. This can help you identify unnecessary columns that are consuming resources. Steps: Export your Power BI model to a .pbix file. Open the .pbix file in Power BI Desktop. Use the Vertipaq Analyzer tool to analyze the model and review the column usage statistics. C. Query the $System.DISCOVER_STORAGE_TABLE_COLUMN_SEGMENTS dynamic management view (DMV). DMVs: Dynamic Management Views (DMVs) provide detailed information about the operations of your Power BI models. Specifically, the $System.DISCOVER_STORAGE_TABLE_COLUMN_SEGMENTS DMV can give you insights into the storage and usage patterns of individual columns within your model.

   Source: Adapted from community discussion — 282b85d

   References

   • Microsoft Learn

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8. Question 98 · 190 in bank

   HOTSPOT - You have the source data model shown in the following exhibit.

   

   The primary keys of the tables are indicated by a key symbol beside the columns involved in each key. You need to create a dimensional data model that will enable the analysis of order items by date, product, and customer. What should you include in the solution? To answer, select the appropriate options in the answer area. NOTE: Each correct selection is worth one point.

   

   This is an interactive task on the real exam — a drag-and-drop, hotspot or ordering question. There is nothing to select here, so the answer is shown below for reference.

   Show the answer figure

   Answer: See figure below

   Explanation

   I think what they are trying to get at is that you denormalize the company information into both tables so that you do not need the CompanyID anymore, which would then make the relationship between OrderItem and Product only based on ProductID. Hence I think A and C. But honestly that whole model is just weird.

   Source: Adapted from community discussion — thuss

   

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9. Question 99 · 190 in bank

   Select 2

   You have a Fabric tenant that contains a semantic model named Model1. Model1 uses Import mode. Model1 contains a table named Orders. Orders has 100 million rows and the following fields.

   

   You need to reduce the memory used by Model1 and the time it takes to refresh the model. Which two actions should you perform? Each correct answer presents part of the solution. NOTE: Each correct answer is worth one point.

   • ASplit OrderDateTime into separate date and time columns.
   • BReplace TotalQuantity with a calculated column.
   • CConvert Quantity into the Text data type.
   • DReplace TotalSalesAmount with a measure.

   Show answer

   Answer: AD

   Explanation

   I was under the impression that A should be correct due to the fact that separate date and time column achieve higher columnar redundancy and allow better data compression. Whereas solution B, an additional calculated column, would inflate the memory usage. Measure are not stored in memory and would therefore be favorable concerning the stated objective of reducing memory used and minimizing refresh times.

   Source: Adapted from community discussion — fabric1

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10. Question 100 · 190 in bank

    Select 2

    You have a Fabric tenant that contains a semantic model. You need to prevent report creators from populating visuals by using implicit measures. What are two tools that you can use to achieve the goal? Each correct answer presents a complete solution. NOTE: Each correct answer is worth one point.

    • AMicrosoft Power BI Desktop
    • BTabular Editor
    • CMicrosoft SQL Server Management Studio (SSMS)
    • DDAX Studio

    Show answer

    Answer: AB

    Explanation

    AB: To prevent report creators from populating visuals using implicit measures in a Power BI semantic model within a Fabric tenant, you can utilize the following tools: 1. Tabular Editor: 2. Power BI Desktop (Data Model View):

    Source: Adapted from community discussion — IshtarSQL

    References

    • Microsoft Learn

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Keyboard:J Kmove between questionsA–Dselect an answerRshow or hide the answer← →change page

DP-600 practice, question 91. 190 questions available.