ExpertHard1 markMultiple Choice
AZ-305 · Question 20 · Domain 2.2: Data Integration
You are designing a data analytics platform using Azure Synapse Analytics.
The data science team needs to query 50 TB of historical log data stored in Azure Data Lake Storage Gen2. They only run these queries sporadically, typically at the end of the month, to generate ad-hoc reports. They require a solution that uses standard T-SQL but minimizes costs by only charging for the queries executed, rather than paying for continuous compute capacity.
Which Synapse Analytics component should you recommend?
You are designing a data analytics platform using Azure Synapse Analytics.
The data science team needs to query 50 TB of historical log data stored in Azure Data Lake Storage Gen2. They only run these queries sporadically, typically at the end of the month, to generate ad-hoc reports. They require a solution that uses standard T-SQL but minimizes costs by only charging for the queries executed, rather than paying for continuous compute capacity.
Which Synapse Analytics component should you recommend?
Answer options:
A.
Dedicated SQL pool
B.
Serverless SQL pool
C.
Apache Spark pool
D.
Azure Data Explorer
How to approach this question
Match 'sporadic queries', 'T-SQL', and 'pay per query' to the serverless offering in Synapse.
Full Answer
B.Serverless SQL pool✓ Correct
Serverless SQL pool
Azure Synapse Analytics offers two types of SQL pools. A Dedicated SQL pool provisions compute resources and charges you for uptime, which is best for consistent, heavy data warehousing workloads. A Serverless SQL pool is an on-demand query service that allows you to query data directly in Azure Data Lake Storage using T-SQL. You are only charged for the data processed by the queries you run, making it the most cost-effective solution for sporadic, ad-hoc reporting.
Common mistakes
Choosing Dedicated SQL pool. Candidates often assume large data (50 TB) requires a dedicated pool, but serverless is specifically designed for ad-hoc data lake exploration.
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