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Exam Code: DP-600
Exam Questions: 169
Microsoft Certified: Fabric Analytics Engineer Associate
Updated: 15 Apr, 2026
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Practicing : 1 - 5 of 169 Questions
Question 1

You have a Fabric tenant that contains two workspaces named Woritspace1 and Workspace2. Workspace1 contains a lakehouse named Lakehouse1. Workspace2 contains a lakehouse named Lakehouse2. Lakehouse! contains a table named dbo.Sales. Lakehouse2 contains a table named dbo.Customers. You need to ensure that you can write queries that reference both dbo.Sales and dbo.Customers in the same SQL query without making additional copies of the tables. What should you use?  

Options :
Answer: A

Question 2

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? 

Options :
Answer: C

Question 3

You have a Fabric tenant that contains a warehouse. Several times a day. the performance of all warehouse queries degrades. You suspect that Fabric is throttling the compute used by the warehouse. What should you use to identify whether throttling is occurring? 

Options :
Answer: D

Question 4

You have a Fabric tenant. You are creating a Fabric Data Factory pipeline. You have a stored procedure that returns the number of active customers and their average sales for the current month. You need to add an activity that will execute the stored procedure in a warehouse. The returned values must be available to the downstream activities of the pipeline. Which type of activity should you add?Script

Options :
Answer: A

Question 5

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?

Options :
Answer: B

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