Amazon Athena Query Result Reuse for Cost Optimization
A financial company wants to use Amazon Athena to run on-demand SQL queries on a petabyte-scale dataset to support a business intelligence (BI) application. An AWS Glue job that runs during non-business hours updates the dataset once every day. The BI application has a standard data refresh frequency of 1 hour to comply with company policies. A data engineer wants to cost optimize the company's use of Amazon Athena without adding any additional infrastructure costs. Which solution will meet these requirements with the LEAST operational overhead?
Community Votes
100% of anonymous learners picked answer B. Votes are pick records left by other test-takers — they are not the verified answer.
Community Insight
The question tests knowledge of Athena's native cost-saving features versus data engineering optimizations, with the common trap being the selection of Apache Parquet (D) due to its general performance benefits rather than addressing the specific 'identical query' scenario.
This page explains how to optimize Amazon Athena costs using the built-in query result reuse feature, which caches results from identical queries executed within a short timeframe. It establishes that this solution meets cost and operational overhead requirements better than format conversion or infrastructure changes.
Many candidates choose Option D (Apache Parquet) because it significantly reduces scan size and improves query speed generally. However, this requires an additional ETL step (Glue job change), increasing operational overhead and failing the 'least operational overhead' constraint compared to the zero-config reuse feature.
Community Discussion (7 comments)
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Expert Analysis
Why the Answer Is Correct
Option B is correct because Amazon Athena’s Query Result Reuse feature automatically caches the results of SQL queries executed within the last 24 hours. When an identical query is run again, Athena returns the cached result instead of scanning the underlying S3 data, effectively reducing the cost to $0 for repeated scans. This aligns perfectly with the requirement for a standard data refresh frequency where the same BI queries are likely executed hourly against the same daily dataset. It requires no additional infrastructure setup.Why the Other Options Are Wrong
Option A is incorrect because moving data to Glacier Deep Archive introduces retrieval latency and costs, which does not directly reduce Athena query costs for active data and adds complexity. Option C is incorrect because adding ElastiCache introduces significant operational overhead and ongoing costs, violating the 'no additional infrastructure costs' constraint. Option D is incorrect while beneficial for performance, converting data to Parquet requires modifying the Glue job logic, which increases operational overhead compared to simply enabling a feature flag like query reuse.Community Comment Notes
The community heavily favored Option B, with users noting that query reuse is designed specifically for scenarios where identical queries are re-run. One commenter highlighted that Parquet helps all queries but doesn't address the 'least overhead' constraint as well as reuse. Another user pointed out that since the Glue job updates once a day, the data remains static for that day, making repeated queries ideal candidates for caching.Official Reference
Exam Strategy
When asked for 'least operational overhead,' prioritize AWS managed features that require configuration changes over architectural changes like format conversions or new service deployments. Always check if the scenario involves repeated identical operations, which often signals cache or reuse features.
Frequently Asked Questions
Why isn't changing to Parquet the best answer?
Parquet reduces scan costs but requires modifying the Glue job, increasing operational overhead. Query reuse has zero extra overhead.
Does Query Result Reuse work for different queries?
No, it only works for identical queries (same text) executed within 24 hours. It does not help with varied BI dashboards.
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