Quick Answer
The answer is AWS Glue Crawler. This is the correct choice because a Glue Crawler automatically scans data in Amazon S3, infers the schema—including data types—and detects the partition structure by analyzing the folder hierarchy, such as year/month/day directories for date-based partitions. It then populates the AWS Glue Data Catalog with this metadata, allowing ETL jobs to read the data without any manual schema definition. On the AWS Certified Data Engineer Associate DEA-C01 exam, this question tests your understanding of how to automate schema and partition detection for continuously updated, partitioned data; a common trap is confusing the Crawler with Glue ETL jobs or the Data Catalog itself, but remember the Crawler is the discovery engine that feeds the Catalog. Memory tip: think of the Crawler as a “schema scout” that climbs the folder tree to map partitions and columns automatically.
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 data engineering team needs to transform CSV files stored in Amazon S3 into Parquet format using AWS Glue. The files are partitioned by date and are updated hourly. Which AWS Glue feature should be used to automatically detect the schema and partition structure?
AWS Glue Crawler
Discovers schema and partitions automatically.
AWS Glue DataBrew
Why wrong: For visual data preparation, not schema discovery.
AWS Lake Formation
Why wrong: For data lake access control.
Amazon Athena
Why wrong: Query service, does not discover schema.
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 Crawler
AWS Glue Crawler is the correct choice because it automatically scans data in S3, infers the schema (including data types), and detects the partition structure (e.g., date-based partitions like year/month/day) by examining the folder hierarchy. It then populates the AWS Glue Data Catalog with metadata, enabling ETL jobs to read the data without manual schema definition.
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.
AWS Glue Crawler
Why this is correct
Discovers schema and partitions automatically.
Related concept
Read the scenario before looking for a memorised answer.
AWS Glue DataBrew
Why it's wrong here
For visual data preparation, not schema discovery.
AWS Lake Formation
Why it's wrong here
For data lake access control.
Amazon Athena
Why it's wrong here
Query service, does not discover schema.
Common exam traps
AWS often tests the distinction between tools that discover metadata (Crawler) versus tools that consume or transform data (Athena, DataBrew), leading candidates to pick Athena because it can query partitioned data, but it cannot automatically detect the partition structure without a pre-existing catalog.
Detailed technical explanation
AWS Glue Crawler uses a classifier to infer schema from sample data, and it recognizes partition patterns by default (e.g., 'year=2025/month=03/day=15') in S3 key prefixes. A subtle behavior is that crawlers can be configured to add new partitions incrementally without re-scanning the entire dataset, which is critical for hourly updates to avoid unnecessary cost and time.
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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Data Ingestion and Transformation — study guide chapter
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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 Crawler — AWS Glue Crawler is the correct choice because it automatically scans data in S3, infers the schema (including data types), and detects the partition structure (e.g., date-based partitions like year/month/day) by examining the folder hierarchy. It then populates the AWS Glue Data Catalog with metadata, enabling ETL jobs to read the data without manual schema definition.
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 30, 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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