DEA-C01 Data Store Management Practice Question
A data engineer is designing a data lake on Amazon S3 to store JSON logs from an application. The logs are written once and never modified. The engineer needs to query the data using Amazon Athena with the best performance and lowest cost. The engineer wants to partition the data by year, month, and day based on the log timestamp. Which approach should the engineer use to organize the S3 objects?
⚠ Common exam trap
The trap here is assuming that any S3 organization can be partitioned by simply defining a table schema, without the physical prefix structure that enables partition pruning.
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 a Hive-style prefix structure such as s3://bucket/year=YYYY/month=MM/day=DD/ and define partitions in the AWS Glue Data Catalog.
Organizing data in a Hive-style prefix hierarchy allows Athena and AWS Glue to recognize partitions. When queries filter on partition columns, only relevant prefixes are scanned, reducing data scanned and cost. This is the recommended practice for time-series data in S3. Other options either do not create real partitions or fail to provide efficient pruning.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Enable S3 Inventory to generate a manifest of objects and use it to create a partitioned table in Athena.
Why it's wrong here
S3 Inventory provides a list of objects and metadata, but it does not physically reorganize data into partitions. Using it to create a partitioned table would still require the underlying objects to be in a partitioned prefix structure. Without that, Athena cannot prune data effectively, so this does not meet the performance and cost goals.
- ✓
Use a Hive-style prefix structure such as s3://bucket/year=YYYY/month=MM/day=DD/ and define partitions in the AWS Glue Data Catalog.
Why this is correct
This approach creates a hierarchical prefix that Athena and AWS Glue recognize as partitions. When queries filter on year, month, or day, Athena prunes irrelevant partitions, scanning less data and lowering cost. It is the standard, efficient way to organize time-series data in S3 for query engines like Athena and Redshift Spectrum.
- ✗
Store objects in a flat structure and create an AWS Glue crawler to automatically add partitions based on file metadata.
Why it's wrong here
A flat structure without partition prefixes does not allow a crawler to infer partitions. Crawlers can detect partitions only if the S3 path contains key-value pairs like year=2024. Without that, the table has no partitions, so Athena scans all objects for each query, leading to higher cost and slower performance.
- ✗
Store all objects in a single prefix and create a table with a partition projection based on the timestamp column.
Why it's wrong here
Partition projection works with a partitioned table, not a single prefix. Storing all objects in one prefix forces Athena to scan all data for every query, increasing cost and reducing performance. Projection cannot infer partitions without a structured prefix hierarchy, so this approach fails to provide the required pruning.
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 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.