DEA-C01 Data Store Management Practice Question
A data engineer is using AWS Glue to catalog data stored in Amazon S3. The data is in Parquet format and partitioned by year, month, and day. The engineer needs to ensure that AWS Glue crawlers correctly identify the partitions and that Amazon Athena queries can efficiently prune partitions. Which action should the engineer take?
⚠ Common exam trap
The trap here is assuming that custom classifiers or manual partition creation are needed for standard Hive-style partitions, when the crawler handles them automatically.
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
✓
Store the data in a directory structure like s3://bucket/year=2023/month=01/day=01/, and run the crawler with the default settings to automatically detect partitions.
The correct action is to store data using Hive-style partition paths (e.g., year=2023/month=01/day=01/) and run the AWS Glue crawler with default settings. The crawler will automatically detect the partitions and update the Data Catalog. Athena can then use partition pruning to optimize queries. Other options either use unnecessary custom classifiers, manual partition management, or irrelevant settings.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Store the data in a directory structure like s3://bucket/year=2023/month=01/day=01/, and run the crawler with the default settings to automatically detect partitions.
Why this is correct
AWS Glue crawlers automatically detect Hive-style partitions when the S3 path follows the key=value format, such as year=2023/month=01/day=01. This allows the crawler to populate the AWS Glue Data Catalog with partition metadata. Athena can then use this metadata to prune partitions during queries, improving performance and reducing cost by scanning only relevant data.
- ✗
Enable AWS Glue Data Catalog encryption and set the 'classification' property to 'parquet' in the table definition.
Why it's wrong here
Enabling Data Catalog encryption is a security measure and does not affect partition discovery. Setting the classification to 'parquet' is done automatically by the crawler when it detects Parquet format, but it does not ensure partition detection. This option does not address the partitioning structure or how to enable partition pruning in Athena.
- ✗
Configure the crawler to use a custom classifier that recognizes the partition structure, and set the table property 'partition_filtering.enabled' to true.
Why it's wrong here
Custom classifiers are used to parse non-standard data formats, not for standard partition structures like year/month/day. The property 'partition_filtering.enabled' is not a valid table property for Athena partition pruning. This option introduces unnecessary complexity and does not address the correct method for partition discovery and pruning.
- ✗
Manually create the table in the AWS Glue Data Catalog with partition keys, and use AWS Glue ETL jobs to add partitions as new data arrives.
Why it's wrong here
Manually creating the table and partitions is possible but not the most efficient or recommended approach when crawlers can automatically detect partitions. Using ETL jobs to add partitions adds unnecessary overhead and complexity. This method is error-prone and does not leverage the crawler's ability to automatically discover and update partition metadata as new data is added.
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
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.