AWS Certified Data Engineer Associate DEA-C01 Practice Question
An online brokerage maintains a 120-TB Amazon Redshift data warehouse on DS2 nodes. Most queries analyze the latest 6 months of data, but analysts occasionally run reports across the full 8-year history. Leadership wants to cut storage costs, keep all data online without unload or reload tasks, and retain sub-second performance on recent data. Which Amazon Redshift approach satisfies these requirements most cost-effectively?
Convert the fact tables to interleaved sort keys so that queries on recent partitions scan fewer blocks.
Migrate the cluster to RA3 nodes and rely on Amazon Redshift managed storage to automatically tier cold data to Amazon S3.
Enable concurrency scaling on the existing DS2 cluster to add transient compute capacity during peak reporting hours.
Keep the DS2 cluster but unload all data older than 6 months to Amazon S3 and query it through Redshift Spectrum.
RA3 node types decouple compute from storage by using Amazon Redshift managed storage (RMS). Frequently accessed blocks remain on local high-performance SSDs, while infrequently accessed blocks are transparently tiered to Amazon S3. This keeps all data online, maintains fast performance for the recent 6-month "hot" set, and usually lets customers run fewer nodes than DS2 clusters, reducing overall cost. Redshift Spectrum requires manually unloading or partitioning data in S3 and can slow frequently accessed queries. Concurrency scaling addresses query volume, not storage cost. Changing sort keys improves query speed but does not lower the expense of keeping 120 TB on DS2 disks.
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AWS Certified Data Engineer Associate DEA-C01
Data Store Management
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