Quick Answer
The correct answer is AWS Glue crawlers. This is because Glue crawlers are specifically designed to connect to data stores like Amazon S3, inspect the data structure, and automatically infer the schema, which is then stored in the AWS Glue Data Catalog. For the scenario of converting JSON to Parquet, the ETL job reads the inferred schema from the Data Catalog, allowing it to correctly map and transform the data format without manual schema definition. On the AWS Certified Data Engineer Associate DEA-C01 exam, this question tests your understanding of the Glue ecosystem’s core components, often appearing as a distractor against Athena or S3 Select. A common trap is confusing schema inference with querying—remember that Athena queries data but does not infer schemas for ETL jobs. To lock in the concept, use the memory tip: “Crawlers catalog the schema, ETL jobs use the map.”
This DEA-C01 practice question tests your understanding of data ingestion and transformation. Match the stated requirement to the specific cloud service, access model, or configuration option — many options are valid in isolation but not for this scenario. 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 uses AWS Glue ETL jobs to transform data in Amazon S3. The data arrives in JSON format but needs to be converted to Parquet for efficient querying. Which AWS Glue feature should be used to infer the schema and generate transformation code?
Amazon S3 Select
Why wrong: S3 Select retrieves subsets of data but does not infer schemas.
Amazon Athena
Why wrong: Athena queries data but does not infer schemas for Glue ETL jobs.
Amazon Kinesis Data Analytics
Why wrong: Kinesis Data Analytics processes streaming data, not batch schema inference.
AWS Glue crawlers
Crawlers populate the Data Catalog with schema information used by Glue ETL jobs.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
AWS Glue crawlers
Option B is correct because AWS Glue crawlers populate the Data Catalog with table metadata and schema, and the ETL job can use that schema to convert JSON to Parquet. Option A is wrong because Athena is for querying, not schema inference. Option C is wrong because S3 Select operates on individual files. Option D is wrong because Kinesis Data Analytics is for streaming.
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 S3 Select
Why it's wrong here
S3 Select retrieves subsets of data but does not infer schemas.
Amazon Athena
Why it's wrong here
Athena queries data but does not infer schemas for Glue ETL jobs.
Amazon Kinesis Data Analytics
Why it's wrong here
Kinesis Data Analytics processes streaming data, not batch schema inference.
AWS Glue crawlers
Why this is correct
Crawlers populate the Data Catalog with schema information used by Glue ETL jobs.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Many certification questions include familiar terms but test a specific constraint. Read the exact wording before choosing an answer that is generally true but wrong for this case.
Detailed technical explanation
This question should be treated as a scenario, not a definition check. Identify the problem, the constraint and the best action. Then compare each option against those facts.
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
Identify which DEA-C01 exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
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FAQ
Data Ingestion and Transformation — This question tests Data Ingestion and Transformation — Read the scenario before looking for a memorised answer..
The correct answer is: AWS Glue crawlers — Option B is correct because AWS Glue crawlers populate the Data Catalog with table metadata and schema, and the ETL job can use that schema to convert JSON to Parquet. Option A is wrong because Athena is for querying, not schema inference. Option C is wrong because S3 Select operates on individual files. Option D is wrong because Kinesis Data Analytics is for streaming.
Identify which DEA-C01 exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 20, 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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