You are implementing a batch dataset in the Parquet format.
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?

A. Use Snappy compression for the files.

B. Use OPENROWSET to query the Parquet files.

C. Create an external table that contains a subset of columns from the Parquet files.

D. Store all data as string in the Parquet files.








 

Suggested Answer: C

An external table points to data located in Hadoop, Azure Storage blob, or Azure Data Lake Storage. External tables are used to read data from files or write data to files in Azure Storage. With Synapse SQL, you can use external tables to read external data using dedicated SQL pool or serverless SQL pool.
Reference:
https://docs.microsoft.com/en-us/azure/synapse-analytics/sql/develop-tables-external-tables

This question is in DP-203 Data Engineering on Microsoft Azure Exam
For getting Microsoft Certified: Azure Data Engineer Associate Certificate


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