DEA-C01 Data Store Management Practice Question
A data engineer is designing a data lake on Amazon S3. The data is ingested from multiple sources in Parquet format, and the schema evolves over time. Which approach allows querying the data with Amazon Athena while supporting schema evolution?
⚠ Common exam trap
Candidates often think S3 Select or Redshift Spectrum can handle schema evolution automatically, but they lack the schema inference and versioning capabilities that AWS Glue Data Catalog provides for Athena.
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 Data Catalog with crawlers to automatically update the table schema.
AWS Glue Data Catalog with crawlers automatically infers and updates the table schema as new Parquet files with evolving schemas are ingested into S3. This allows Athena to query the data using the latest schema without manual intervention, making it the ideal solution for schema evolution in a data lake.
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 AWS Glue Data Catalog with crawlers to automatically update the table schema.
Why this is correct
AWS Glue crawlers inspect Parquet data in Amazon S3 and populate the AWS Glue Data Catalog, which Athena queries. When new columns appear, re-running the crawler updates the table definition, satisfying the schema evolution requirement without manual DDL.
- ✗
Define Hive-style partitions in Athena and manually update the schema.
Why it's wrong here
Hive-style partitions only organise storage paths; they do not track column-level schema changes, so each new field still requires manual ALTER TABLE work and queries break on drifted Parquet files. It is tempting because partitioning is genuinely the right choice for pruning large datasets by date or region, but it addresses query cost, not schema evolution.
- ✗
Use S3 Select to query the data directly without a schema.
Why it's wrong here
S3 Select filters individual objects using SQL and returns rows, but it has no table catalog, so it cannot join across files or reconcile differing Parquet schemas. It is tempting because it queries data in place without defining a schema, which suits one-off extraction from a single known object, not a governed, evolving data lake queried through Athena.
- ✗
Use Amazon Redshift Spectrum with external tables and update the schema manually.
Why it's wrong here
Redshift Spectrum reads external tables but relies on the same AWS Glue Data Catalog definitions, so schema changes still demand manual updates and queries fail against evolved Parquet. It is tempting because Spectrum queries S3 directly and suits joining lake data with Redshift warehouse tables, yet it adds no automatic schema reconciliation.
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
Senior Network & Security Engineer · founder of Courseiva
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.