- A
Use Athena only, without a catalog, by directly querying files
Why wrong: Athena requires a catalog; schema evolution is not supported automatically.
- B
Use Amazon EMR to process data and write to a Hive metastore
Why wrong: Adds complexity; Glue is simpler for Athena integration.
- C
Use AWS Glue Crawlers to automatically create and update the Glue Data Catalog
Crawlers automatically detect schema changes and update the catalog.
- D
Manually create tables in Athena using DDL statements
Why wrong: Requires manual intervention for schema changes.
Question 1,058 of 1,755
Automating Schema Discovery with AWS Glue Crawlers
This MLS-C01 practice question tests your understanding of data engineering. 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 is building a data lake on Amazon S3. Raw data is ingested from multiple sources in different formats (CSV, JSON, Parquet). The data must be cataloged and made queryable using Amazon Athena. The data schema may evolve over time. Which approach minimizes manual effort and supports schema evolution?
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
✓
Use AWS Glue Crawlers to automatically create and update the Glue Data Catalog
AWS Glue Crawlers automatically infer schema from data in S3, create and update the Glue Data Catalog tables, and handle schema evolution by detecting changes such as new columns or partitions. This minimizes manual effort because the crawler runs on a schedule or trigger, and the catalog is natively integrated with Athena for querying without any additional setup.
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.
- ✗
Use Athena only, without a catalog, by directly querying files
Why it's wrong here
Athena requires a catalog; schema evolution is not supported automatically.
- ✗
Use Amazon EMR to process data and write to a Hive metastore
Why it's wrong here
Adds complexity; Glue is simpler for Athena integration.
- ✓
Use AWS Glue Crawlers to automatically create and update the Glue Data Catalog
Why this is correct
Crawlers automatically detect schema changes and update the catalog.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Manually create tables in Athena using DDL statements
Why it's wrong here
Requires manual intervention for schema changes.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates may think Athena can query files directly without a catalog (Option A), but Athena relies on the Glue Data Catalog (or an external Hive metastore) to map file locations and schemas, making a catalog mandatory for querying.
Detailed technical explanation
How to think about this question
AWS Glue Crawlers use a classifier to infer schema from data formats like CSV, JSON, and Parquet, and they can detect partition structures (e.g., year/month/day) in S3 paths. The crawler updates the Glue Data Catalog with new partitions and schema changes, and it can be configured to add new columns or update existing ones based on a policy (e.g., 'Add new columns only' or 'Update all'). This is critical for data lakes where schemas evolve frequently, as it avoids manual DDL updates and ensures Athena queries remain consistent.
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
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FAQ
Questions learners often ask
What does this MLS-C01 question test?
Data Engineering — This question tests Data Engineering — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Use AWS Glue Crawlers to automatically create and update the Glue Data Catalog — AWS Glue Crawlers automatically infer schema from data in S3, create and update the Glue Data Catalog tables, and handle schema evolution by detecting changes such as new columns or partitions. This minimizes manual effort because the crawler runs on a schedule or trigger, and the catalog is natively integrated with Athena for querying without any additional setup.
What should I do if I get this MLS-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: Jul 4, 2026
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