For reducing query load on Azure SQL during full semantic model refreshes, which deployment pipeline adjustment should you configure?

For reducing query load on Azure SQL during full semantic model refreshes, which deployment pipeline adjustment should you configure?

• A. Increase timeout
• B. Enable data caching
• C. Add a deployment parameter rule to filter the data
• D. Use a different SQL database

Answer: (C)

During a full semantic model refresh, the amount of data read from Azure SQL is the main driver of query load. By configuring a deployment parameter rule to filter the data, you limit the refresh to a subset of data, significantly reducing the volume of queries the database must handle. This approach lets you refresh the model more efficiently, especially in development or staged scenarios where you don’t need the entire dataset loaded each time. Increasing timeout, caching, or switching databases don’t directly reduce the data being queried during the refresh, so they don’t address the load in the same effective way. So, applying a deployment parameter rule to filter the data is the best way to cut the query load during full semantic model refreshes.

During a full semantic model refresh, the amount of data read from Azure SQL is the main driver of query load. By configuring a deployment parameter rule to filter the data, you limit the refresh to a subset of data, significantly reducing the volume of queries the database must handle. This approach lets you refresh the model more efficiently, especially in development or staged scenarios where you don’t need the entire dataset loaded each time. Increasing timeout, caching, or switching databases don’t directly reduce the data being queried during the refresh, so they don’t address the load in the same effective way. So, applying a deployment parameter rule to filter the data is the best way to cut the query load during full semantic model refreshes.