Practice Free DP-600 Exam Online Questions
You have a Fabric tenant that contains 30 CSV files in OneLake. The files are updated daily.
You create a Microsoft Power Bl semantic model named Modell that uses the CSV files as a data source. You configure incremental refresh for Model 1 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?
- A . Query folding is occurring.
- B . Only refresh complete days is selected.
- C . XMLA Endpoint is set to Read Only.
- D . Query folding is NOT occurring.
- E . The data type of the column used to partition the data has changed.
D
Explanation:
A possible cause for the failure is that query folding is NOT occurring (D). Query folding helps optimize refresh by pushing down the query logic to the source system, reducing the amount of data processed and transferred, hence conserving resources.
Reference = The Power BI documentation on incremental refresh and query folding provides detailed information on this topic.
You have a Fabric tenant that contains a workspace named Workspace^ Workspacel is assigned to a Fabric capacity.
You need to recommend a solution to provide users with the ability to create and publish custom Direct Lake semantic models by using external tools. The solution must follow the principle of least privilege.
Which three actions in the Fabric Admin portal should you include in the recommendation? Each correct answer presents part of the solution. NOTE: Each correct answer is worth one point.
- A . From the Tenant settings, set Allow XMLA Endpoints and Analyze in Excel with on-premises datasets to Enabled
- B . From the Tenant settings, set Allow Azure Active Directory guest users to access Microsoft Fabric to Enabled
- C . From the Tenant settings, select Users can edit data models in the Power Bl service.
- D . From the Capacity settings, set XMLA Endpoint to Read Write
- E . From the Tenant settings, set Users can create Fabric items to Enabled
- F . From the Tenant settings, enable Publish to Web
ACD
Explanation:
For users to create and publish custom Direct Lake semantic models using external tools, following the principle of least privilege, the actions to be included are enabling XMLA Endpoints (A), editing data models in Power BI service (C), and setting XMLA Endpoint to Read-Write in the capacity settings (D).
Reference = More information can be found in the Admin portal of the Power BI service documentation, detailing tenant and capacity settings.
You have a Fabric tenant that contains a warehouse.
A user discovers that a report that usually takes two minutes to render has been running for 45 minutes and has still not rendered.
You need to identify what is preventing the report query from completing.
Which dynamic management view (DMV) should you use?
- A . sys.dm-exec_requests
- B . sys.dn_.exec._sessions
- C . sys.dm._exec._connections
- D . sys.dm_pdw_exec_requests
D
Explanation:
The correct DMV to identify what is preventing the report query from completing is sys.dm_pdw_exec_requests (D). This DMV is specific to Microsoft Analytics Platform System (previously known as SQL Data Warehouse), which is the environment assumed to be used here. It provides information about all queries and load commands currently running or that have recently run.
Reference = You can find more about DMVs in the Microsoft documentation for Analytics Platform System.
DRAG DROP
You have a Fabric tenant that contains a Microsoft Power Bl report named Report 1.
Report1 is slow to render. You suspect that an inefficient DAX query is being executed.
You need to identify the slowest DAX query, and then review how long the query spends in the formula engine as compared to the storage engine.
Which five actions should you perform in sequence? To answer, move the appropriate actions from the list of actions to the answer area and arrange them in the correct order.
Explanation:
To identify the slowest DAX query and analyze the time it spends in the formula engine compared to the storage engine, you should perform the following actions in sequence:
From Performance analyzer, capture a recording.
View the Server Timings tab.
Enable Query Timings and Server Timings. Run the query.
View the Query Timings tab.
Sort the Duration (ms) column in descending order by DAX query time.
HOTSPOT
You have a Fabric tenant that contains a semantic model named model1.
The two largest columns in model1 are shown in the following table.
You need to optimize model 1.
The solution must meet the following requirements:
• Reduce the model size.
• Increase refresh performance when using Import mode.
• Ensure that the datetime value for each sales transaction is available in the model.
What should you do on each column? To answer, select the appropriate options in the answer area. NOTE: Each correct selection is worth one point.
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.show()
Does this meet the goal?
- A . Yes
- B . No
B
Explanation:
The df.show() method also does not meet the goal. It is used to show the contents of the DataFrame, not to compute statistical functions.
Reference = The usage of the show() function is documented in the PySpark API documentation.
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
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.
Reference = The usage and output of the EXPLAIN command can be found in the Spark SQL documentation.
HOTSPOT
You have a Fabric tenant that contains a warehouse named WH1. You run the following T-SQL query against WH1.
For each of the following statements, select Yes if the statement is true. Otherwise, select No. NOTE: Each correct selection is worth one point.
You have a Microsoft Power Bl semantic model that contains measures. The measures use multiple calculate functions and a filter function.
You are evaluating the performance of the measures.
In which use case will replacing the filter function with the keepfilters function reduce execution time?
- A . when the filter function uses a nested calculate function
- B . when the filter function references a column from a single table that uses Import mode
- C . when the filter function references columns from multiple tables
- D . when the filter function references a measure
A
Explanation:
The KEEPFILTERS function modifies the way filters are applied in calculations done through the CALCULATE function. It can be particularly beneficial to replace the FILTER function with KEEPFILTERS when the filter context is being overridden by nested CALCULATE functions, which may remove filters that are being applied on a column. This can potentially reduce execution time because KEEPFILTERS maintains the existing filter context and allows the nested CALCULATE functions to be evaluated more efficiently.
Reference: This information is based on the DAX reference and performance optimization guidelines
in the Microsoft Power BI documentation.
DRAG DROP
You are implementing two dimension tables named Customers and Products in a Fabric warehouse.
You need to use slowly changing dimension (SCO) to manage the versioning of data.
The solution must meet the requirements shown in the following table.
Which type of SCD should you use for each table? To answer, drag the appropriate SCD types to the correct tables. Each SCD type may be used once, more than once, or not at all. You may need to drag the split bar between panes or scroll to view content. NOTE: Each correct selection is worth one point.
Explanation:
For the Customers table, where the requirement is to create a new version of the row, you would use:
Type 2 SCD: This type allows for the creation of a new record each time a change occurs, preserving the history of changes over time.
For the Products table, where the requirement is to overwrite the existing value in the latest row, you would use:
Type 1 SCD: This type updates the record directly, without preserving historical data.