Free Practice Microsoft DP-600 Exam Questions 2025

Note: This question is part of a series of questions that present the same scenario. Each question in the series contains a unique solution that might meet the stated goals. Some question sets might have more than one correct solution, while others might not have a correct solution.

After you answer a question in this section, you will NOT be able to return to it. As a result, these questions will not appear in the review screen.

You have a Fabric tenant that contains a lakehouse 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:

REFRESH TABLE customer -

Does this meet the goal?

In optimizing enterprise-scale semantic models, what technique assists in managing access to specific rows of data based on user roles or permissions?

You have a Fabric tenant that contains JSON files in OneLake. The files have one billion items.

You plan to perform time series analysis of the items.

You need to transform the data, visualize the data to find insights, perform anomaly detection, and share the insights with other business users. The solution must meet the following requirements:

• Use parallel processing.

• Minimize the duplication of data.

• Minimize how long it takes to load the data.

What should you use to transform and visualize the data?

You have a Fabric tenant.

You plan to create a data pipeline named Pipeline1. Pipeline1 will include two activities that will execute in sequence.

You need to ensure that a failure of the first activity will NOT block the second activity.

Which conditional path should you configure between the first activity and the second activity?

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.