AWS Certified Data Engineer Associate DEA-C01 Practice Question
An ecommerce company uses an Amazon Redshift RA3 cluster. A BI query joins two 200-GB Redshift tables with an Aurora PostgreSQL orders table through a federated query. Grafana runs the query every minute, causing 10-second latency and high Aurora CPU. Data may be 5 minutes old, and the team wants the lowest ongoing cost. What should the data engineer do?
Replace the query with a standard Redshift view and rely on the query result cache for most dashboard requests.
Unload the two Redshift tables to Amazon S3, create external tables, and use Redshift Spectrum to join them with the federated orders table.
Create a materialized view that joins the Redshift and federated tables, and schedule REFRESH MATERIALIZED VIEW every 5 minutes with Amazon EventBridge. Point the dashboard to the materialized view.
Use AWS DMS and COPY to load the orders table into Redshift every 5 minutes, then keep the dashboard query unchanged.
Creating a materialized view inside Redshift stores the pre-computed join result locally. Because materialized views that reference federated tables can't use auto-refresh, schedule REFRESH MATERIALIZED VIEW every 5 minutes (for example, with an EventBridge rule or Redshift Scheduler). The dashboard then queries the materialized view, delivering sub-second latency while reducing Aurora workload to one refresh per 5-minute window. Standard views or the result cache would still hit Aurora each minute, COPY/DMS loads replicate data unnecessarily, and a Spectrum approach adds S3 costs without reducing Aurora load.
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What is a materialized view in Redshift?
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What is a materialized view in Amazon Redshift?
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How does federated querying work in Amazon Redshift?
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What is Amazon EventBridge, and how is it used to schedule tasks?
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AWS Certified Data Engineer Associate DEA-C01
Data Store Management
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