MLS-C01 Data Engineering Practice Question
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?
⚠ Common exam trap
A common mix-up: candidates 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.
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.
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
Managing a self-hosted Hive metastore on Amazon EMR requires manual configuration of metadata tables and infrastructure maintenance, failing to automate the schema discovery required for evolving data formats. This approach is intended for large-scale, complex ETL workloads where fine-grained control over Spark or Presto clusters is necessary. You would use EMR when you need to perform heavy transformations on massive datasets rather than simply cataloguing existing S3 objects.
- ✓
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.
- ✗
Manually create tables in Athena using DDL statements
Why it's wrong here
Requires manual intervention for schema changes.
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 |
Go deeper
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Written by Johnson Ajibi, MSc IT Security
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