Answer: Connect to Pool1 and query sys.dm_pdw_nodes_db_partition_stats.

## Analysis of Data Skew Detection in Azure Synapse Analytics To identify data skew in a dedicated SQL pool table, the correct approach involves connecting to the specific dedicated SQL pool (Pool1) and querying the appropriate system dynamic management view (DMV). ### Why Option D is Correct: - **sys.dm_pdw_nodes_db_partition_stats** is specifically designed for Azure Synapse Analytics dedicated SQL pools and provides detailed information about data distribution across compute nodes - This DMV returns page and row count information for every partition in the current database, allowing you to analyze how data is distributed across the 60 distributions in a dedicated SQL pool - By connecting directly to Pool1, you ensure you're querying the actual dedicated SQL pool where Table1 resides, giving you accurate statistics about the data distribution - The view shows data distribution across all nodes, making it ideal for identifying skew patterns where some distributions have significantly more data than others ### Why Other Options Are Incorrect: **Option A**: Connecting to the built-in pool and querying sys.dm_pdw_nodes_db_partition_stats - The built-in pool refers to the serverless SQL pool, which cannot access dedicated SQL pool statistics - Serverless pools don't have access to the detailed distribution statistics of dedicated pools **Option B**: Connecting to the built-in pool and running DBCC CHECKALLOC - DBCC CHECKALLOC is primarily for checking database allocation consistency, not for analyzing data distribution skew - Again, the built-in pool cannot access dedicated pool statistics **Option C**: Connecting to Pool1 and querying sys.dm_pdw_node_status - This DMV provides information about node health and status, not about data distribution across partitions - It doesn't contain the necessary row count and page count information needed to measure data skew ### Best Practice Approach: The optimal method for analyzing data skew involves: 1. Connecting directly to the dedicated SQL pool (Pool1) 2. Querying sys.dm_pdw_nodes_db_partition_stats to get distribution-level statistics 3. Calculating the coefficient of variation or comparing maximum/minimum row counts across distributions 4. Identifying distributions with significantly higher data volumes than others This approach provides the most accurate and actionable information for addressing data skew issues in dedicated SQL pools.

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