Use sys.dm_pdw_nodes_db_partition_stats to analyze any skewness in the data.
Reference:
https://docs.microsoft.com/en-us/azure/synapse-analytics/sql-data-warehouse/cheat-sheet Basic Concept: This question tests monitor, configure, and optimize database resources in the DP-300 exam context. The correct choice is determined by the exact service boundary and operational requirement stated in the scenario.
Why D is Correct: The pdw_nodes dynamic management views apply to Azure Synapse Analytics or Parallel Data Warehouse nodes, not ordinary Azure SQL Database troubleshooting. This fits the case because the requested outcome is: You need to identify the extent of the data skew in Table1. The selected feature addresses that outcome directly rather than relying on a workaround.
Why A is Wrong: The pdw_nodes dynamic management views apply to Azure Synapse Analytics or Parallel Data Warehouse nodes, not ordinary Azure SQL Database troubleshooting. 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: Connect to the built-in pool and run DBCC CHECKALLOC. 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 C is Wrong: Connect to Pool1 and run DBCC CHECKALLOC. 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 would produce a different operational signal than the one needed to investigate, alert, or tune the workload in this question.