AWS Certified Data Engineer - Associate DEA-C01Demo

By Amazon · Question Mode

QuestionQ189

Data Operations and Support

A company operates a central Amazon Redshift data warehouse that consolidates daily transactional data from Amazon RDS for PostgreSQL and Amazon Aurora MySQL. A data engineer observes that certain complex transformation queries require hours to complete. The data engineer needs to optimize query performance to reduce query execution time as much as possible.

Which solution meets this requirement?

  • A Increase the concurrency scaling quota for the Redshift cluster.
  • B Export the tables to an Amazon S3 bucket. Use Amazon Athena to query the data in the bucket.
  • C Use Amazon Redshift Spectrum to create external tables based on the Redshift tables.
  • D Use materialized views in Amazon Redshift for frequently queried data patterns.
Explanation

Amazon Redshift materialized views store precomputed query results. For frequent, predictable complex transformations involving joins or aggregations, Redshift can return the stored results instead of repeatedly executing the expensive work against base tables, substantially reducing execution time. Concurrency scaling primarily addresses queued concurrent workload capacity, not the computation within a long-running transformation query.

Learn more

Community Discussion

No comments yet. Be the first to start the discussion!