DEA-C01 · Question #281
A company maintains a central Amazon Redshift data warehouse that aggregates daily transactional data from Amazon RDS for PostgreSQL and Amazon Aurora MySQL. A data engineer notices that some…
The correct answer is D. Use materialized views in Amazon Redshift for frequently queried data patterns. Amazon Redshift materialized views precompute and store results of complex queries, allowing subsequent queries to run much faster by reading from the precomputed data. This significantly reduces execution time for frequently used transformation patterns.
Question
A company maintains a central Amazon Redshift data warehouse that aggregates daily transactional data from Amazon RDS for PostgreSQL and Amazon Aurora MySQL. A data engineer notices that some complex transformation queries take hours to finish. The data engineer wants to optimize query performance to reduce query execution time as much as possible. Which solution will meet this requirement?
Options
- AIncrease the concurrency scaling quota for the Redshift cluster.
- BExport the tables to an Amazon S3 bucket. Use Amazon Athena to query the data in the bucket.
- CUse Amazon Redshift Spectrum to create external tables based on the Redshift tables.
- DUse materialized views in Amazon Redshift for frequently queried data patterns.
How the community answered
(23 responses)- A4% (1)
- B13% (3)
- C4% (1)
- D78% (18)
Explanation
Amazon Redshift materialized views precompute and store results of complex queries, allowing subsequent queries to run much faster by reading from the precomputed data. This significantly reduces execution time for frequently used transformation patterns.
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