BenchMark Quizzes by MDFT Pro

Identify the Cause of Incremental Refresh Failure

You work for MDFT Pro, a well-known training agency that manages student enrollment data. Your team has created a Power BI semantic model named ENROLLMENT that connects to a large SQL Server database containing 5 years of student records. The model uses incremental refresh to efficiently process only the most recent data changes.

After adding several complex calculated columns and relationships to the model, you notice that the incremental refresh operations are failing with resource exhaustion errors. The refresh process starts successfully but terminates after running for several minutes.

What is a possible cause of the failure?

Choose the correct answer from the options below.

Explanations for each answer:

The refresh window is set to less than 5 years is incorrect. The refresh window setting determines how far back data is refreshed, but it would not cause a resource exhaustion failure. A shorter refresh window would actually use fewer resources.
Query folding is not occurring is correct.
The incremental refresh policy is using a date column is incorrect. Using a date column for incremental refresh is the recommended approach and would not cause resource exhaustion. In fact, it helps optimize refresh performance.
The semantic model is published to a shared capacity is incorrect. While publishing to a shared capacity has limitations, it would result in a different error message about capacity constraints rather than a resource exhaustion failure.
The data source credentials are using Windows authentication is incorrect. The authentication method used for data source credentials would not cause a resource exhaustion failure. It would result in an authentication error if there were issues.

Learn more about Incremental Refresh in Fabric:

Incremental Refresh

Exam Preparation Quiz

Identify the Cause of Incremental Refresh Failure