Quick Answer
The answer is Amazon Athena, as it is the serverless query service purpose-built for running standard SQL directly against data stored in Amazon S3. This is correct because Athena’s architecture is designed to minimize data scanned per query by leveraging columnar formats like Parquet and partitioning strategies, which allow it to skip irrelevant files and columns entirely. On the AWS Certified Solutions Architect Professional SAP-C02 exam, this scenario tests your understanding of cost-optimized, serverless analytics—a common trap is choosing Amazon Redshift Spectrum, but remember that Athena requires no cluster management and is ideal for ad-hoc queries on S3 data lakes. The key memory tip is “Partition and Parquet for performance”: always pair S3 partitioning with Parquet or ORC to reduce Athena’s scan volume, which directly lowers cost and latency.
This SAP-C02 practice question tests your understanding of design for new solutions. 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 is designing a new data lake on Amazon S3. They need to query the data using standard SQL and expect to run complex queries that scan large datasets. The query performance should be optimized to minimize data scanned. Which service should they use?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue: "minimum / minimize"
Why it matters: Asks for the least resource use — fewest addresses, smallest subnet, lowest overhead. Eliminate over-provisioned options even if they would technically work.
Amazon Redshift Spectrum
Why wrong: Redshift Spectrum requires a Redshift cluster and is more complex for pure ad-hoc queries.
Amazon EMR
Why wrong: EMR requires cluster management and is not serverless for SQL.
Amazon Athena
Athena is serverless and can query S3 data with standard SQL, optimized by partitioning and columnar formats.
Amazon QuickSight
Why wrong: QuickSight is a BI tool, not a query engine for raw 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 a serverless interactive query service that uses standard SQL to analyze data directly in Amazon S3. It is optimized for querying large datasets with a pay-per-query model, and it automatically minimizes data scanned by leveraging features like columnar data formats (Parquet, ORC), partitioning, and compression to reduce the amount of data read per query.
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 Redshift Spectrum
Why it's wrong here
Redshift Spectrum requires a Redshift cluster and is more complex for pure ad-hoc queries.
Amazon EMR
Why it's wrong here
EMR requires cluster management and is not serverless for SQL.
Amazon Athena
Why this is correct
Athena is serverless and can query S3 data with standard SQL, optimized by partitioning and columnar formats.
Clue confirmation
The clue word "minimum / minimize" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
Amazon QuickSight
Why it's wrong here
QuickSight is a BI tool, not a query engine for raw data.
Common exam traps
The trap here is that candidates often confuse Amazon Redshift Spectrum with Athena because both query S3 data, but Redshift Spectrum requires a running Redshift cluster and is not serverless, while Athena is fully serverless and designed specifically for minimizing data scanned in a data lake scenario.
Detailed technical explanation
Athena uses Presto under the hood, a distributed SQL query engine, and automatically pushes down predicates and projections to S3 to minimize data scanned. It integrates with AWS Glue Data Catalog for schema management and supports partitioning at the S3 prefix level, allowing queries to skip irrelevant partitions entirely. In real-world scenarios, using columnar formats like Parquet with Snappy compression can reduce data scanned by up to 90% compared to CSV, directly lowering query costs.
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
Design for New Solutions — This question tests Design for New Solutions — Read the scenario before looking for a memorised answer..
The correct answer is: Amazon Athena — Amazon Athena is a serverless interactive query service that uses standard SQL to analyze data directly in Amazon S3. It is optimized for querying large datasets with a pay-per-query model, and it automatically minimizes data scanned by leveraging features like columnar data formats (Parquet, ORC), partitioning, and compression to reduce the amount of data read per query.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Yes — watch for: "minimum / minimize". Asks for the least resource use — fewest addresses, smallest subnet, lowest overhead. Eliminate over-provisioned options even if they would technically work.
Read the scenario before looking for a memorised answer.
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Same concept, more angles
These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.
Variation 1. A company is designing a data lake on AWS using Amazon S3. They need to run SQL queries on the data without moving it to a separate database. Which AWS service should they use?
mediumWhy B: Option D is correct because Amazon Athena allows serverless SQL queries directly on S3 data. Option A is wrong because Redshift is a data warehouse, requires loading data. Option B is wrong because EMR is for big data processing. Option C is wrong because Glue is an ETL service.
Variation 2. A company is designing a data lake on AWS using Amazon S3. They need to query the data using standard SQL without moving it to a separate analytics store. Which AWS service should they use?
mediumWhy A: Option C is correct because Amazon Athena allows querying data directly in S3 using standard SQL. Option A (Redshift Spectrum) also queries S3 but requires Redshift cluster. Option B (Glue) is for ETL, not querying. Option D (QuickSight) is for visualization.
Last reviewed: Jun 24, 2026
This SAP-C02 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 SAP-C02 exam.
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