The performance capacity of a query is determined by the user ' s resource class.
Smaller resource classes reduce the maximum memory per query, but increase concurrency.
Larger resource classes increase the maximum memory per query, but reduce concurrency.
Reference:
https://docs.microsoft.com/en-us/azure/synapse-analytics/sql-data-warehouse/resource-classes-for- workloadmanagement Basic Concept: This question tests performance optimization for SQL workloads, including Query Store, automatic tuning, index/statistics maintenance, and engine-level resource behavior.
Why C is Correct: Assign a larger resource class to the automated data load queries. is related to operational monitoring or tuning, but it must match the exact signal needed: query history, resource utilization, wait
/blocking detail, or automatic remediation. This fits the case because the requested outcome is: You have an Azure data solution that contains an enterprise data warehouse in Azure Synapse Analytics named DW1. The selected feature addresses that outcome directly rather than relying on a workaround.
Why A is Wrong: Assign a smaller resource class to the automated data load queries. is related to operational monitoring or tuning, but it must match the exact signal needed: query history, resource utilization, wait
/blocking detail, or automatic remediation. It does not expose the required metric, query history, wait/blocking signal, or tuning mechanism; using it would not give the administrator the evidence requested.
Why B is Wrong: Create sampled statistics to every column in each table of DW1. is related to operational monitoring or tuning, but it must match the exact signal needed: query history, resource utilization, wait
/blocking detail, or automatic remediation. It is useful in other troubleshooting paths, but this scenario requires a more specific monitoring or optimization feature.
Why D is Wrong: Hash distribute the large fact tables in DW1 before performing the automated data loads. is related to operational monitoring or tuning, but it must match the exact signal needed: query history, resource utilization, wait/blocking detail, or automatic remediation. It does not expose the required metric, query history, wait/blocking signal, or tuning mechanism; using it would not give the administrator the evidence requested.