DEA-C01 Data Store Management Practice Question
A data engineer is designing an Amazon S3 data lake and needs to enforce schema-on-read for a dataset that is queried by Amazon Athena. The data is stored as Parquet files partitioned by year, month, and day. The engineer wants to minimize the amount of data scanned by queries that filter on a specific date range. Which approach should the engineer take?
⚠ Common exam trap
Test-takers frequently confuse Parquet column pruning and predicate pushdown with partition pruning, assuming file format alone eliminates scanning of non-matching dates.
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
✓
Create an AWS Glue Data Catalog table with partition keys year, month, and day, and run MSCK REPAIR TABLE or use partition projection before querying in Athena.
Athena uses the AWS Glue Data Catalog to resolve table schemas and partition locations. When the year, month, and day columns are declared as partition keys and the partitions are either registered or projected, date filters cause Athena to read only the matching S3 prefixes. Storing everything in one prefix, converting to CSV, or moving to Redshift Spectrum does not achieve the same partition-pruning benefit.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Create an AWS Glue Data Catalog table with partition keys year, month, and day, and run MSCK REPAIR TABLE or use partition projection before querying in Athena.
Why this is correct
Registering the partitions in the Data Catalog lets Athena prune partitions that do not match the date filter, so only the relevant S3 prefixes are read. Partition projection can compute partition locations from the table properties without running a repair step, which is more scalable. Either way, partition pruning is what minimizes scanned bytes.
- ✗
Store all Parquet files in a single prefix and rely on Parquet column pruning to skip dates.
Why it's wrong here
Parquet column pruning only avoids reading columns not referenced in the query; it does not skip rows based on a date filter unless row-group statistics and predicate pushdown apply, and even then all files in the prefix must be listed and opened. Without partition keys, Athena scans every file, which defeats the goal of minimizing scanned bytes.
- ✗
Convert the dataset to CSV and add a WHERE clause on the date column in every query.
Why it's wrong here
CSV is row-oriented and uncompressed by default, so Athena must read entire rows and cannot prune columns. A WHERE clause on a non-partition column still requires reading all data before filtering. This increases scanned bytes compared with Parquet and provides no partition-level pruning, so it worsens the stated performance goal.
- ✗
Create an Amazon Redshift Spectrum external table and query it from Redshift instead of Athena.
Why it's wrong here
Redshift Spectrum can query S3 data and supports partition pruning, but it requires an active Redshift cluster and shifts the workload away from Athena, which the scenario specifies. It also does not inherently reduce the data scanned unless the external table is partitioned correctly. It adds infrastructure cost without directly addressing the Athena requirement.
Visual reference
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
Related to this question
About these practice questions
This DEA-C01 question is part of Courseiva's 1,321-question bank — original exam-style content with full explanations and wrong-answer analysis, never real exam questions or exam dumps. Learn why practice questions differ from exam dumps →
JA
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.