Answer: Connect to Pool1 and query sys.dm_pdw_nodes_db_partition_stats.

To identify data skew in a dedicated SQL pool table in Azure Synapse Analytics, the optimal approach is to query the `sys.dm_pdw_nodes_db_partition_stats` dynamic management view while connected to the specific dedicated SQL pool (Pool1). This view provides detailed information about data distribution across the compute nodes, including row counts and storage usage per distribution, which is essential for detecting data skew. **Why Option D is correct:** - **Connection to Pool1**: Data skew analysis must be performed within the dedicated SQL pool context where the table resides, as each dedicated SQL pool operates as an independent MPP (Massively Parallel Processing) system. - **sys.dm_pdw_nodes_db_partition_stats**: This DMV specifically exposes partition-level statistics across all compute nodes, allowing you to compare row counts and data sizes between distributions to quantify skew. **Why other options are incorrect:** - **Option A**: Connecting to the built-in pool (serverless SQL pool) is incorrect because it cannot access dedicated SQL pool-specific DMVs or analyze data distribution within Pool1. - **Option B**: DBCC CHECKALLOC is used for checking database allocation consistency, not for measuring data skew across distributions in a dedicated SQL pool. - **Option C**: This combines the incorrect connection context (built-in pool) with an inappropriate tool (DBCC CHECKALLOC) for data skew analysis. **Best Practice Approach:** To effectively identify data skew, you would query `sys.dm_pdw_nodes_db_partition_stats` filtered by your specific table, then analyze the row count variance between distributions. Significant differences in row counts indicate data skew that may impact query performance.

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