DEA-C01 Data Ingestion and Transformation Practice Question
A data engineer is configuring an AWS Glue crawler to catalog data stored in an Amazon S3 bucket. The data is partitioned by year, month, and day in a Hive-style structure (for example, s3://bucket/data/year=2023/month=01/day=15/). The engineer wants the crawler to recognize the partitions and add them to the AWS Glue Data Catalog. What should the engineer do?
⚠ Common exam trap
The trap here is assuming that partition detection is automatic regardless of crawler configuration or include path scope.
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
✓
Ensure the crawler's include path points to the bucket root and that partition detection is enabled in the crawler configuration.
AWS Glue crawlers can automatically detect Hive-style partitions when the include path covers the partitioned data and partition detection is enabled. The crawler reads the key=value structure in the S3 prefixes, creates partition metadata, and updates the Data Catalog. This enables partition pruning in query engines like Amazon Athena and Redshift Spectrum.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Manually create a partitioned table in the AWS Glue Data Catalog using the AWS CLI before running the crawler.
Why it's wrong here
Manually creating a partitioned table bypasses the crawler's automatic schema and partition discovery, which defeats the purpose of using a crawler. It also requires the engineer to define the schema and partition keys without the crawler's inference. While manual table creation is possible, it is not the recommended approach when a crawler can automatically detect the Hive-style partitions present in the S3 structure.
- ✗
Configure the crawler with the 'Add new columns only' option and enable partition detection.
Why it's wrong here
The 'Add new columns only' option controls schema update behavior for columns, not partition detection. Partition detection is a separate crawler setting. Enabling this option alone does not ensure that Hive-style partitions are recognized. The crawler must have partition detection explicitly configured, and the schema update option does not influence whether partitions are discovered and added to the Data Catalog.
- ✓
Ensure the crawler's include path points to the bucket root and that partition detection is enabled in the crawler configuration.
Why this is correct
AWS Glue crawlers automatically detect Hive-style partitions when the include path points to the root of the partitioned data and partition detection is enabled. The crawler parses the key=value structure in the S3 prefixes and adds partition metadata to the Data Catalog. This allows queries in Athena and Redshift Spectrum to use partition pruning for efficiency.
- ✗
Create a separate crawler for each partition prefix and run them individually.
Why it's wrong here
Creating separate crawlers for each partition prefix is operationally inefficient and does not scale as new partitions are added. It also does not leverage the crawler's built-in ability to detect Hive-style partitions automatically. The scenario requires a single crawler configuration that recognizes the partition structure, so this approach adds unnecessary management overhead without addressing the core requirement.
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 |
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Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official Amazon Web Services exam blueprint
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