Which AWS data warehouse handles spiky QuickSight direct queries with least overhead?

DEA-C01 By islever Editorial Last updated Oct 17, 2026

Choose a data store. Analyze data by using AWS services.

Short answer

A Use Amazon Redshift Serverless to load the data into Redshift managed storage, providing an auto-scaling data warehouse for spiky QuickSight direct queries.

Question

A company uses a variety of AWS and third-party data stores. The company wants to consolidate all the data into a central data warehouse to perform analytics. Users need fast response times for analytics queries. The company uses Amazon QuickSight in direct query mode to visualize the data. Users normally run queries during a few hours each day with unpredictable spikes. Which solution will meet these requirements with the LEAST operational overhead?

Use Amazon Redshift Serverless to load all the data into Amazon Redshift managed storage (RMS). Correct Answer
Use Amazon Athena to load all the data into Amazon S3 in Apache Parquet format.
Use Amazon Redshift provisioned clusters to load all the data into Amazon Redshift managed storage (RMS).
Use Amazon Aurora PostgreSQL to load all the data into Aurora.

Key Takeaways

This tests selecting a low-ops analytics warehouse for intermittent, spiky QuickSight direct queries; the trap is assuming Athena's serverless S3 querying satisfies a central data warehouse requirement.

Amazon Redshift Serverless consolidates data into Redshift managed storage and automatically scales for unpredictable QuickSight direct-query workloads, making it the lowest-overhead data warehouse choice here. The page confirms option A and explains why provisioned Redshift, Athena, and Aurora fall short.

Common mistake: Choosing Athena (B) because it is serverless, without noting that it queries data in S3 rather than loading into a central warehouse and may not provide the required fast, consistent analytics response times.

Expert Analysis

Why the Answer Is Correct

Amazon Redshift Serverless is designed for exactly this pattern: it loads data into Redshift managed storage, automatically scales capacity up and down with query load, and removes cluster sizing and provisioning from the team. QuickSight direct query against Redshift benefits from a columnar, massively parallel analytics engine rather than an OLTP database. Because usage is limited to a few hours per day with unpredictable spikes, the serverless model also avoids paying for idle provisioned capacity. As ceaser221 noted, it "automatically scales resources up or down based on query workload," which directly addresses the operational-overhead requirement.

Why the Other Options Are Wrong

Athena (B) is serverless for querying data in S3, but it does not load data into a central data warehouse, and performance depends on file layout, partitioning, and ongoing optimization. Provisioned Redshift (C) can meet the analytics requirement but requires cluster sizing, concurrency management, and capacity planning for unpredictable spikes, so it has more operational overhead. Aurora PostgreSQL (D) is an OLTP database, not a columnar analytics warehouse; using it as the central analytics store for spiky QuickSight direct queries would likely produce slower responses and require instance management.

Community Comment Notes

All visible votes and comments support option A, with MerryLew emphasizing that with serverless you "don't have to bother with manual provisioning." 7a1d491 succinctly frames the fit: "Serverless is for unpredictable loads." The consensus reflects the exam's key distinction between a managed serverless warehouse and provisioned clusters or query-in-place S3.

Official Reference

https://docs.aws.amazon.com/redshift/latest/mgmt/serverless-whatis.html

This link opens the official vendor documentation in a new tab.

Exam Strategy

When "least operational overhead" and "unpredictable spikes" appear alongside QuickSight direct query, favor a serverless analytics warehouse over provisioned clusters or query-in-place S3. Verify that the option actually consolidates data into a central data warehouse, not just storage or an OLTP database.

Frequently Asked Questions

Why is Athena not the least-overhead choice for a central data warehouse?

Athena queries data in S3 but does not load it into a central data warehouse; performance also depends on Parquet layout, partitioning, and ongoing tuning, which adds operational work.

Does Redshift Serverless support QuickSight direct query for spiky workloads?

Yes. QuickSight can use direct query against Redshift Serverless, and the serverless warehouse automatically scales capacity for intermittent query spikes without manual provisioning.

More DEA-C01 FAQ →

Community Votes

100%

100% of anonymous learners picked answer A. Votes are pick records left by other test-takers — they are not the verified answer.

Discussion 3 contributions

Compiled from public community threads and reader submissions, lightly edited for clarity. Handles and vote tallies are shown as recorded, and comments reflect the views of individual contributors.

From public community threads 3

ceaser221 👍 1 Answered A

Redshift Serverless automatically scales resources up or down based on query workload. This eliminates the need for manual capacity provisioning and scaling, significantly reducing operational overhead.
MerryLew 👍 1 Answered A

A so you don't have to bother with manual provisioning.
7a1d491 👍 1 Answered A

Serverless is for unpredictable loads

Reader comments & corrections

No reader corrections yet — spotted an error or have a note? Share it below.

Practice All DEA-C01 Questions

Access 100 questions with complete answers and detailed explanations.

View Full DEA-C01 Practice Test →

← Back to DEA-C01 Study Guide