- A
s3://bucket/events/2024-01-01/event_type=data.parquet
Why wrong: Flat date prefix without partition keys; Athena cannot prune partitions effectively.
- B
s3://bucket/2024/01/01/event_type/events/data.parquet
Why wrong: Directory structure mimicking file system but missing partition key-value pairs; Athena requires Hive-style for partition discovery.
- C
s3://bucket/event_type=events/year=2024/month=01/day=01/data.parquet
Correct Hive-style partitioning with logical key order (year, month, day) and event type, enabling efficient partition pruning.
- D
s3://bucket/day=01/month=01/year=2024/event_type=events/data.parquet
Why wrong: Option D uses Hive-style partitioning but with an unconventional key order (day, month, year, event_type). While Athena can still read this structure with proper table definitions, the non-standard order may complicate automatic partition discovery and is not the most appropriate choice. Option C is preferred because it follows the typical convention of coarse-to-fine partition order.
DEA-C01 Data Store Management Practice Question
This DEA-C01 practice question tests your understanding of data store management. This is a configuration task: choose the command set that satisfies every stated requirement. Small differences — like 'secret' vs 'password' or 'transport input ssh' vs 'all' — change whether the answer is correct. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
A data engineer is designing a data lake on Amazon S3. The data is ingested from multiple sources and needs to be partitioned by year, month, day, and event type for efficient querying with Amazon Athena. Which S3 key prefix structure is most appropriate?
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
s3://bucket/event_type=events/year=2024/month=01/day=01/data.parquet
Option C uses Hive-style partitioning (event_type=events/year=2024/month=01/day=01), which Athena and other query engines natively support. This structure allows Athena to perform partition pruning, reading only the relevant directories based on WHERE clause filters, significantly reducing data scanned and improving query performance. Option D also uses Hive-style partitioning but with a different order of partition keys (day, month, year). While still valid, this non-standard order may cause issues with automatic partition discovery when using MSCK REPAIR TABLE, which expects the partition order to match the table definition. Therefore, option C is the most appropriate because it follows the common convention of listing partitions from coarse to fine granularity (year > month > day) and can be easily loaded into Athena without additional configuration.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
s3://bucket/events/2024-01-01/event_type=data.parquet
Why it's wrong here
Flat date prefix without partition keys; Athena cannot prune partitions effectively.
- ✗
s3://bucket/2024/01/01/event_type/events/data.parquet
Why it's wrong here
Directory structure mimicking file system but missing partition key-value pairs; Athena requires Hive-style for partition discovery.
- ✓
s3://bucket/event_type=events/year=2024/month=01/day=01/data.parquet
Why this is correct
Correct Hive-style partitioning with logical key order (year, month, day) and event type, enabling efficient partition pruning.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
s3://bucket/day=01/month=01/year=2024/event_type=events/data.parquet
Why it's wrong here
Option D uses Hive-style partitioning but with an unconventional key order (day, month, year, event_type). While Athena can still read this structure with proper table definitions, the non-standard order may complicate automatic partition discovery and is not the most appropriate choice. Option C is preferred because it follows the typical convention of coarse-to-fine partition order.
Common exam traps
Common exam trap: answer the scenario, not the keyword
AWS often tests the distinction between Hive-style partitioning (key=value) and flat or date-only prefixes, where candidates mistakenly choose a structure that does not support partition pruning or is incompatible with Athena's partition discovery.
Detailed technical explanation
How to think about this question
Hive-style partitioning stores metadata in the AWS Glue Data Catalog or Hive Metastore, enabling Athena to automatically discover partitions and use partition projection for even faster queries. Under the hood, Athena reads the partition columns from the S3 prefix path, so a query like WHERE year=2024 AND event_type='events' will only list objects under the matching prefix, avoiding a full table scan. In real-world scenarios, using date and event type partitions is critical for cost optimization, as Athena charges per TB of data scanned.
KKey Concepts to Remember
- Read the scenario before looking for a memorised answer.
- Find the constraint that changes the correct option.
- Eliminate answers that are true in general but not in this case.
TExam Day Tips
- Watch for words such as best, first, most likely and least administrative effort.
- Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
A media company stores terabytes of video archives that are accessed once a year for audit purposes. Moving these objects to a cold storage tier (Azure Archive, S3 Glacier, or Google Nearline) costs a fraction of hot storage. Questions like this test whether you understand storage tiers, access frequency tradeoffs, and retrieval latency requirements.
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 |
What to study next
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FAQ
Questions learners often ask
What does this DEA-C01 question test?
Data Store Management — This question tests Data Store Management — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: s3://bucket/event_type=events/year=2024/month=01/day=01/data.parquet — Option C uses Hive-style partitioning (event_type=events/year=2024/month=01/day=01), which Athena and other query engines natively support. This structure allows Athena to perform partition pruning, reading only the relevant directories based on WHERE clause filters, significantly reducing data scanned and improving query performance. Option D also uses Hive-style partitioning but with a different order of partition keys (day, month, year). While still valid, this non-standard order may cause issues with automatic partition discovery when using MSCK REPAIR TABLE, which expects the partition order to match the table definition. Therefore, option C is the most appropriate because it follows the common convention of listing partitions from coarse to fine granularity (year > month > day) and can be easily loaded into Athena without additional configuration.
What should I do if I get this DEA-C01 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 30, 2026
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