Quick Answer
Amazon Athena is the correct choice because it provides serverless SQL analytics directly on data stored in Amazon S3, eliminating the need for infrastructure management while delivering low-latency query results. This service works by leveraging a distributed SQL engine that can automatically scale to handle time-series sensor data, scanning only the necessary partitions or files when using efficient data formats like Parquet or columnar compression. On the AWS Certified Data Engineer Associate DEA-C01 exam, this scenario tests your understanding of serverless analytics versus provisioned services like Amazon Redshift or EMR, with a common trap being to select Redshift for its SQL capabilities despite its infrastructure overhead. Remember that Athena is purpose-built for ad-hoc queries on S3 data without any servers to manage, making it ideal for time-series analysis where you need instant SQL access without provisioning clusters. A helpful memory tip is to think of Athena as the “S3 SQL scanner” — it reads what’s already in the bucket, never moves the data.
This DEA-C01 practice question tests your understanding of data store management. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
A company stores time-series sensor data in Amazon S3. They need to query the data using SQL with minimal latency and no infrastructure management. Which service should they use?
Amazon Kinesis Data Analytics
Why wrong: Kinesis Data Analytics processes streaming data, not batch queries on S3.
Amazon Athena
Athena is serverless and directly queries S3 using SQL.
Amazon Redshift
Why wrong: Redshift requires provisioning and managing clusters.
Amazon DynamoDB
Why wrong: DynamoDB is a NoSQL database, not designed for SQL queries on S3 data.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
Amazon Athena
Amazon Athena is the correct choice because it is a serverless interactive query service that allows you to analyze data directly in Amazon S3 using standard SQL without any infrastructure to manage. It is optimized for querying structured, semi-structured, and unstructured data stored in S3, making it ideal for time-series sensor data with minimal latency requirements.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
For each option: why learners choose it and why it is or isn't the right answer here.
Amazon Kinesis Data Analytics
Why it's wrong here
Kinesis Data Analytics processes streaming data, not batch queries on S3.
Amazon Athena
Why this is correct
Athena is serverless and directly queries S3 using SQL.
Related concept
Read the scenario before looking for a memorised answer.
Amazon Redshift
Why it's wrong here
Redshift requires provisioning and managing clusters.
Amazon DynamoDB
Why it's wrong here
DynamoDB is a NoSQL database, not designed for SQL queries on S3 data.
Common exam traps
The trap here is that candidates often confuse Amazon Athena with Amazon Redshift Spectrum, but the question explicitly requires 'no infrastructure management,' which eliminates Redshift; also, Kinesis Data Analytics is mistakenly chosen by those who think it can query static S3 data, but it is strictly for real-time streams.
Detailed technical explanation
Athena uses Presto under the hood to execute SQL queries, leveraging a distributed SQL engine that reads data directly from S3 via the S3 Select API for columnar formats like Parquet and ORC, which can reduce data scanned and improve performance. It supports partitioning and compression to further minimize latency and cost, making it suitable for petabyte-scale time-series datasets without any cluster setup or maintenance.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
A media company stores terabytes of video archives that are accessed once a year for audit purposes. Moving these objects to a cold storage tier (Azure Archive, S3 Glacier, or Google Nearline) costs a fraction of hot storage. Questions like this test whether you understand storage tiers, access frequency tradeoffs, and retrieval latency requirements.
What to study next
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FAQ
Data Store Management — This question tests Data Store Management — Read the scenario before looking for a memorised answer..
The correct answer is: Amazon Athena — Amazon Athena is the correct choice because it is a serverless interactive query service that allows you to analyze data directly in Amazon S3 using standard SQL without any infrastructure to manage. It is optimized for querying structured, semi-structured, and unstructured data stored in S3, making it ideal for time-series sensor data with minimal latency requirements.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 11, 2026
This DEA-C01 practice question is part of Courseiva's free Amazon Web Services certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the DEA-C01 exam.
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