ExpertHard1 markMultiple Choice
AZ-305 · Question 20 · Domain 2.2: Data Integration
A data engineering team is building a data lake architecture on Azure. They have petabytes of raw data stored in Azure Data Lake Storage Gen2 in Parquet format.
Data scientists need to run ad-hoc, exploratory SQL queries directly against this data. The query workload is highly unpredictable—some days there are hundreds of complex queries, and other days there are none. The company wants to pay only for the queries executed, without provisioning dedicated compute clusters that sit idle.
Which Azure Synapse Analytics component should you recommend?
A data engineering team is building a data lake architecture on Azure. They have petabytes of raw data stored in Azure Data Lake Storage Gen2 in Parquet format.
Data scientists need to run ad-hoc, exploratory SQL queries directly against this data. The query workload is highly unpredictable—some days there are hundreds of complex queries, and other days there are none. The company wants to pay only for the queries executed, without provisioning dedicated compute clusters that sit idle.
Which Azure Synapse Analytics component should you recommend?
Answer options:
A.
Dedicated SQL pool
B.
Serverless SQL pool
C.
Apache Spark pool
D.
Azure Data Factory
How to approach this question
Match 'ad-hoc SQL queries directly on data lake' and 'pay only for queries executed' to the serverless compute model.
Full Answer
B.Serverless SQL pool✓ Correct
Azure Synapse Analytics Serverless SQL pool is a distributed data processing system built for large-scale data and computational functions. It allows you to query data in your data lake (like Parquet files) using familiar T-SQL syntax without provisioning or managing infrastructure. You are billed only for the data processed by the queries you run, making it perfect for unpredictable, exploratory workloads.
Common mistakes
Choosing Dedicated SQL pool, which is designed for traditional enterprise data warehousing with predictable, continuous workloads.
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