AWS Certified Data Engineer Associate DEA-C01 Practice Question
The analytics team uses an 8-node Amazon Redshift DC2.large cluster to run ad-hoc reports against a 500-GB star schema that grows by about 10 GB per day. During business hours roughly 20 concurrent queries now finish in 2 minutes instead of 5 seconds. Management wants sub-10-second response times without doubling the monthly cost. Which solution best meets these requirements?
Resize the cluster to eight DC2.8xlarge nodes to increase CPU, memory, and local SSD storage.
Replace the cluster with Amazon RDS for PostgreSQL and create four read replicas for parallel query processing.
Unload the tables to Amazon S3 in Parquet format and switch the reports to query the data with Amazon Athena.
Migrate the cluster to RA3 nodes of similar compute size, enable Concurrency Scaling, and allow Amazon Redshift Managed Storage to hold the historical data.
RA3 nodes run on newer hardware, feature large high-performance SSD caches, and use Amazon Redshift Managed Storage (RMS) to keep less-frequently accessed blocks in low-cost S3. Migrating to a comparable RA3 cluster therefore adds capacity without paying for extra SSDs and typically increases cost by less than 100 percent. Enabling Concurrency Scaling automatically provisions transient compute when concurrency spikes, allowing 20 simultaneous queries to complete within seconds while you pay only for the brief burst. Simply resizing to DC2.8xlarge would almost double compute and storage cost, Athena would still scan large amounts of data and deliver higher latency, and Amazon RDS for PostgreSQL with read replicas is not optimized for large analytic joins.
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What are RA3 nodes in Amazon Redshift?
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How does Concurrency Scaling work in Amazon Redshift?
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What is Amazon Redshift Managed Storage (RMS)?
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AWS Certified Data Engineer Associate DEA-C01
Data Store Management
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