For the "Exam Topics" team:
To begin with, your questions vs answers are completely wrong., period. Check your answer for the question#36 in the same page itself!!! Why you are misleading us who are preparing seriously for the exam?? I need an immediate explanation why these questions Q#36 and Q#38 with different answers being at the same question pattern??? Seriously.
DBCC PDW_SHOWSPACEUSED is a command that can be executed in the context of the dedicated SQL pool (formerly SQL DW). It provides detailed information about how data is distributed across distributions (similar to shards or segments) in the underlying storage of the dedicated SQL pool.
By running this command against Pool1, you can see the distribution of data across the distributions. This includes information about the number of rows per distribution, which helps in identifying data skew.
B is not an option because there's no specific concept of a "built-in pool" in Azure Synapse Analytics dedicated SQL pool context
To identify the extent of data skew in Table1, you should connect to Pool1 and run DBCC PDW_SHOWSPACEUSED.
DBCC PDW_SHOWSPACEUSED is a Dynamic Management View (DMV) that provides information about the physical storage of data in a Parallel Data Warehouse (PDW) instance. This includes the distribution of data across partitions and the amount of space used by each partition.
By running DBCC PDW_SHOWSPACEUSED, you can identify partitions that are storing a disproportionately large amount of data. These partitions may be indicative of data skew.
Connect to Pool1 and run DBCC PDW_SHOWSPACEUSED
Azure Synapse Analytics dedicated SQL pool (formerly known as Azure Synapse Analytics Parallel Data Warehouse) uses a Massively Parallel Processing (MPP) architecture and DBCC PDW_SHOWSPACEUSED is a system stored procedure that can be used to check the distribution of data across the compute nodes. By running this command on Pool1 and specifying the fact table Table1, you can identify the extent of data skew in Table1 and determine if the data is evenly distributed across the compute nodes or if it is skewed towards a specific node
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