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Exam2pass > Microsoft > Microsoft Certifications > DP-600 > DP-600 Online Practice Questions and Answers

DP-600 Online Practice Questions and Answers

Questions 4

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.

A. Use the Analyze in Excel feature.

B. Use the Vertipaq Analyzer tool.

C. Query the $system.discovered_STORAGE_TABLE_COLUMN-iN_SEGMeNTS dynamic management view (DMV).

D. Query the discover_hehory6Rant dynamic management view (DMV).

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Correct Answer: BC

Explanation: The Vertipaq Analyzer tool (B) and querying the $system.discovered_STORAGE_TABLE_COLUMNS_IN_SEGMENTS dynamic management view (DMV) (C) can help identify which columns are frequently loaded into memory. Both methods provide insights into the storage and retrieval aspects of the semantic model. References = The Power BI documentation on Vertipaq Analyzer and DMV queries offers detailed guidance on how to use these tools for performance analysis.

Questions 5

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?

A. DirectQuery

B. Dual

C. Direct Lake

D. Import

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Correct Answer: A

Explanation: When users interact with a report built from a Direct Lake semantic model that uses row-level security (RLS), the DAX queries will operate in DirectQuery mode (A). This is because the model directly queries the underlying data source without importing data into Power BI. References = The Power BI documentation on DirectQuery provides detailed explanations of how RLS and DAX queries function in this mode.

Questions 6

You have a Fabric tenant tha1 contains a takehouse named Lakehouse1. Lakehouse1 contains a Delta table named Customer.

When you query Customer, you discover that the query is slow to execute. You suspect that maintenance was NOT performed on the table.

You need to identify whether maintenance tasks were performed on Customer.

Solution: You run the following Spark SQL statement:

EXPLAIN TABLE customer

Does this meet the goal?

A. Yes

B. No

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Correct Answer: B

Explanation: No, the EXPLAIN TABLE statement does not identify whether maintenance tasks were performed on a table. It shows the execution plan for a query. References = The usage and output of the EXPLAIN command can be found in the Spark SQL documentation.

Questions 7

You have a Fabric warehouse that contains a table named Staging.Sales. Staging.Sales contains the following columns.

You need to write a T-SQL query that will return data for the year 2023 that displays ProductID and ProductName arxl has a summarized Amount that is higher than 10,000. Which query should you use?

A. Option A

B. Option B

C. Option C

D. Option D

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Correct Answer: B

Explanation: The correct query to use in order to return data for the year 2023 that displays ProductID, ProductName, and has a summarized Amount greater than 10,000 is Option B. The reason is that it uses the GROUP BY clause to organize the data by ProductID and ProductName and then filters the result using the HAVING clause to only include groups where the sum of Amount is greater than 10,000. Additionally, the DATEPART(YEAR, SaleDate) = '2023' part of the HAVING clause ensures that only records from the year 2023 are included. References = For more information, please visit the official documentation on T-SQL queries and the GROUP BY clause at T-SQL GROUP BY.

Questions 8

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 Bl Desktop?

A. data categories

B. Data view

C. Model view

D. DAX query view

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Correct Answer: C

Explanation: To create a diagram that contains only the Sales table and related tables, you should use the Model view (C) in Microsoft Power BI Desktop. This view allows you to visualize and manage the relationships between tables within your semantic model. References = Microsoft Power BI Desktop documentation outlines the functionalities available in Model view for managing semantic models.

Questions 9

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

A. At the semantic model level, set Large dataset storage format to Off.

B. At the tenant level, set Create and use Metrics to Enabled.

C. At the semantic model level, set Large dataset storage format to On.

D. At the tenant level, set Data Activator to Enabled.

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Correct Answer: C

Explanation: To enable scale-out for a semantic model, you should first set Large dataset storage format to On (C) at the semantic model level. This configuration is necessary to handle larger datasets effectively in a scaled-out environment. References = Guidance on configuring large dataset storage formats for scale-out is available in the Power BI documentation.

Questions 10

You have source data in a folder on a local computer.

You need to create a solution that will use Fabric to populate a data store. The solution must meet the following requirements:

Support the use of dataflows to load and append data to the data store.

Ensure that Delta tables are V-Order optimized and compacted automatically.

Which type of data store should you use?

A. a lakehouse

B. an Azure SQL database

C. a warehouse

D. a KQL database

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Correct Answer: A

Explanation: A lakehouse (A) is the type of data store you should use. It supports dataflows to load and append data and ensures that Delta tables are Z-Order optimized and compacted automatically. References = The capabilities of a lakehouse and its support for Delta tables are described in the lakehouse and Delta table documentation.

Questions 11

You have a Fabric tenant that contains a new semantic model in OneLake.

You use a Fabric notebook to read the data into a Spark DataFrame.

You need to evaluate the data to calculate the min, max, mean, and standard deviation values for all the string and numeric columns.

Solution: You use the following PySpark expression:

df.explain()

Does this meet the goal?

A. Yes

B. No

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Correct Answer: B

Explanation: The df.explain() method does not meet the goal of evaluating data to calculate statistical functions. It is used to display the physical plan that Spark will execute. References = The correct usage of the explain() function can be found in the PySpark documentation.

Questions 12

You need to provide Power Bl developers with access to the pipeline. The solution must meet the following requirements:

Ensure that the developers can deploy items to the workspaces for Development and Test.

Prevent the developers from deploying items to the workspace for Production.

Follow the principle of least privilege.

Which three levels of access should you assign to the developers? Each correct answer presents part of the solution. NOTE: Each correct answer is worth one point.

A. Build permission to the production semantic models

B. Admin access to the deployment pipeline

C. Viewer access to the Development and Test workspaces

D. Viewer access to the Production workspace

E. Contributor access to the Development and Test workspaces

F. Contributor access to the Production workspace

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Correct Answer: BDE

Explanation: To meet the requirements, developers should have Admin access to the deployment pipeline (B), Contributor access to the Development and Test workspaces (E), and Viewer access to the Production workspace (D). This setup ensures they can perform necessary actions in development and test environments without having the ability to affect production. References = The Power BI documentation on workspace access levels and deployment pipelines provides guidelines on assigning appropriate permissions.

Questions 13

You have a Fabric tenant that contains a warehouse named Warehouse1. Warehouse1 contains a fact table named FactSales that has one billion rows. You run the following TSQL statement.

CREATE TABLE test.FactSales AS CLONE OF Dbo.FactSales;

For each of the following statements, select Yes if the statement is true. Otherwise, select No. NOTE: Each correct selection is worth one point.

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Correct Answer:

A replica of dbo.Sales is created in the test schema by copying the metadata only. - No Additional schema changes to dbo.FactSales will also apply to test.FactSales. - No Additional data changes to dbo.FactSales will also apply to test.FactSales. - Yes The CREATE TABLE AS CLONE statement creates a copy of an existing table, including its data and any associated data structures, like indexes. Therefore, the statement does not merely copy metadata; it also copies the data. However, subsequent schema changes to the original table do not automatically propagate to the cloned table. Any data changes in the original table after the clone operation will not be reflected in the clone unless explicitly updated. References = CREATE TABLE AS SELECT (CTAS) in SQL Data Warehouse

Exam Code: DP-600
Exam Name: Implementing Analytics Solutions Using Microsoft Fabric
Last Update: Jun 08, 2025
Questions: 126

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