- A
AWS Glue Crawler
Discovers schema and partitions automatically.
- B
AWS Glue DataBrew
Why wrong: For visual data preparation, not schema discovery.
- C
AWS Lake Formation
Why wrong: For data lake access control.
- D
Amazon Athena
Why wrong: Query service, does not discover schema.
Question 1,403 of 1,786
DEA-C01 Data Ingestion and Transformation Practice Question
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?
Answer choices
Why each option matters
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
Option-by-option breakdown
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
Common exam trap: answer the scenario, not the keyword
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
How to think about this question
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.
KKey Concepts to Remember
- Read the scenario before looking for a memorised answer.
- Find the constraint that changes the correct option.
- Eliminate answers that are true in general but not in this case.
TExam Day Tips
- Watch for words such as best, first, most likely and least administrative effort.
- Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
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.
Quick reference
AWS S3 Storage Class Comparison
| Storage Class | Min Duration | Retrieval | Use Case |
|---|---|---|---|
| S3 Standard | None | Immediate | Frequently accessed data |
| S3 Standard-IA | 30 days | Immediate | Infrequent access, rapid retrieval |
| S3 One Zone-IA | 30 days | Immediate | Non-critical infrequent data |
| S3 Intelligent-Tiering | None | Immediate–hours | Unknown or changing access patterns |
| S3 Glacier Instant | 90 days | Milliseconds | Archive with instant retrieval |
| S3 Glacier Flexible | 90 days | Minutes–hours | Archive, flexible retrieval |
| S3 Glacier Deep Archive | 180 days | Hours | Long-term compliance archive |
What to study next
Got this wrong? Here's your next step.
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FAQ
Questions learners often ask
What does this DEA-C01 question test?
Data Ingestion and Transformation — This question tests Data Ingestion and Transformation — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
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.
What should I do if I get this DEA-C01 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 30, 2026
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