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Azure Synapse Analytic dedicated SQL pool. The CSV file contains three columns named username, comment, and date.
The data flow already contains the following:
✑ A source transformation.
✑ A Derived Column transformation to set the appropriate types of data.
✑ A sink transformation to land the data in the pool.
You need to ensure that the data flow meets the following requirements:
✑ All valid rows must be written to the destination table.
✑ Truncation errors in the comment column must be avoided proactively.
✑ Any rows containing comment values that will cause truncation errors upon insert must be written to a file in blob storage.
Which two actions should you perform? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.
Data files will be produced be using Azure Data Factory and stored in Azure Data Lake Storage Gen2. The files will be consumed by an Azure Synapse Analytics serverless SQL pool.
You need to minimize storage costs for the solution.
What should you do?
Which type of Databricks cluster should you use?
Which output mode should you use?
✑ TransactionType: 40 million rows per transaction type
✑ CustomerSegment: 4 million per customer segment
✑ TransactionMonth: 65 million rows per month
AccountType: 500 million per account type
You have the following query requirements:
✑ Analysts will most commonly analyze transactions for a given month.
✑ Transactions analysis will typically summarize transactions by transaction type, customer segment, and/or account type
You need to recommend a partition strategy for the table to minimize query times.
On which column should you recommend partitioning the table?
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