DEA-C01 practice question 20 of 25

A company maintains an Amazon Redshift provisioned cluster that the company uses for extract, transform, and load (ETL) operations to support…

A company maintains an Amazon Redshift provisioned cluster that the company uses for extract, transform, and load (ETL) operations to support critical analysis tasks. A sales team within the company maintains a Redshift cluster that the sales team uses for business intelligence (BI) tasks. The sales team recently requested access to the data that is in the ETL Redshift cluster so the team can perform weekly summary analysis tasks. The sales team needs to join data from the ETL cluster with data that is in the sales team's BI cluster. The company needs a solution that will share the ETL cluster data with the sales team without interrupting the critical analysis tasks. The solution must minimize usage of the computing resources of the ETL cluster. Which solution will meet these requirements?

Answer options

A: Set up the sales team Bl cluster as a consumer of the ETL cluster by using Redshift data sharing. Correct answer

B: Create materialized views based on the sales team's requirements. Grant the sales team direct access to the ETL cluster.

C: Create database views based on the sales team's requirements. Grant the sales team direct access to the ETL cluster.

D: Unload a copy of the data from the ETL cluster to an Amazon S3 bucket every week. Create an Amazon Redshift Spectrum table based on the content of the ETL cluster.

Correct Answer:

A. Set up the sales team Bl cluster as a consumer of the ETL cluster by using Redshift data sharing.

Explanation

Amazon Redshift data sharing lets one provisioned cluster (producer) share live, transactionally consistent data with another cluster (consumer) instantly and without copying. Queries run on the consumer’s compute nodes, leaving the producer’s resources free, so the ETL cluster continues critical workloads uninterrupted.

Why Incorrect

B. Exposing materialized views still uses producer compute and allows direct user connections—risks workload contention.
C. Normal views execute on producer cluster, consuming its resources and affecting critical jobs.
D. Weekly unload and Spectrum create duplicate data, add delays, and ongoing S3 and Spectrum administration.

References

1. Amazon Redshift Management Guide, “Data sharing,” paras “Compute isolation” and “No data copy” (2023-10-12).

2. AWS Big Data Blog, “Introducing Amazon Redshift data sharing” (2021-05-17) – explains consumer queries use their own compute.

3. Amazon Redshift Database Developer Guide, “CREATE VIEW” notes views execute on the owner cluster’s resources.

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