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Microsoft DP-600 Exam Sample Questions

Question # 1

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

Answer

B. No

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.

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Question # 2

You are the administrator of a Fabric workspace that contains a lakehouse named Lakehouse1. Lakehouse1 contains the following tables: Table1: A Delta table created by using a shortcut Table2: An external table created by using Spark Table3: A managed table You plan to connect to Lakehouse1 by using its SQL endpoint. What will you be able to do after connecting to Lakehouse1?

A. ReadTable3.

B. Update the data Table3.

C. ReadTable2.

D. Update the data in Table1.

Answer

D. Update the data in Table1.

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Question # 3

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.

Answer

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

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.

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Question # 4

You need to ensure the data loading activities in the AnalyticsPOC workspace are executed in the appropriate sequence. The solution must meet the technical requirements. What should you do?

A. Create a pipeline that has dependencies between activities and schedule the pipeline.

B. Create and schedule a Spark job definition.

C. Create a dataflow that has multiple steps and schedule the dataflow.

D. Create and schedule a Spark notebook.

Answer

A. Create a pipeline that has dependencies between activities and schedule the pipeline.

Explanation:

To meet the technical requirement that data loading activities must ensure the raw and cleansed data is updated completely before populating the dimensional model, you would need a mechanism that allows for ordered execution. A pipeline in Microsoft Fabric with dependencies set between activities can ensure that activities are executed in a specific sequence. Once set up, the pipeline can be scheduled to run at the required intervals (hourly or daily depending on the data source).

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Question # 5

You need to design a semantic model for the customer satisfaction report. Which data source authentication method and mode should you use? To answer, select the appropriate options in the answer area. NOTE: Each correct selection is worth one point.

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