DEA-C01 Data Store Management Practice Question
A data engineer manages an Amazon S3 data lake with millions of small JSON files under prefixes partitioned by year/month/day. Amazon Athena queries scan far more data than expected and return slowly. The engineer wants to reduce bytes scanned and improve query performance while keeping files in S3 and queryable by Athena. Which solution meets these requirements with the LEAST operational overhead?
⚠ Common exam trap
The trap here is assuming that changing storage class or accelerating transfer improves Athena query cost and speed, when the real driver is data format and 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 AWS Glue to convert the JSON files to Apache Parquet, write them to partitioned prefixes, and register the resulting tables in the AWS Glue Data Catalog.
Athena charges and performs based on bytes scanned, and row-oriented JSON forces reading entire objects. Converting to columnar Parquet lets the engine read only referenced columns, while partitioning by year/month/day enables partition pruning so irrelevant dates are skipped. Registering the converted data in the AWS Glue Data Catalog makes it available to Athena with minimal management, directly reducing scanned bytes and latency.
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 Transfer Acceleration on the bucket and configure Athena workgroups to use it for all queries.
Why it's wrong here
Transfer Acceleration speeds up uploads and downloads over long distances by using edge locations; it does not change how Athena reads data during query execution. It cannot reduce bytes scanned or improve columnar pruning, so the slow full-prefix scans and high data scanned charges would remain unchanged.
- ✓
Use AWS Glue to convert the JSON files to Apache Parquet, write them to partitioned prefixes, and register the resulting tables in the AWS Glue Data Catalog.
Why this is correct
Converting to columnar Parquet with partitioning lets Athena read only needed columns and partitions, cutting bytes scanned. Registering the tables in the Data Catalog makes them immediately queryable without managing a separate metastore, and Glue handles the conversion job, keeping operational overhead low compared with self-managed ETL.
- ✗
Attach an S3 Lifecycle policy that transitions the JSON objects to S3 Glacier Instant Retrieval after 30 days and query them through Athena.
Why it's wrong here
Athena can query Glacier Instant Retrieval objects, but the objects remain row-oriented JSON, so Athena still scans all columns and the same volume of data. Storage class changes affect durability cost and retrieval latency, not query scan efficiency, so performance and bytes-scanned charges do not improve.
- ✗
Create an Amazon S3 Inventory report for each partition and use it to rewrite the Athena queries so they reference only the inventory files.
Why it's wrong here
S3 Inventory produces a scheduled CSV or Parquet listing of objects and metadata; it is not a queryable table of the underlying data. Athena cannot scan inventory output as a substitute for the JSON objects, so query results would be missing or wrong. This adds overhead without reducing bytes scanned for the actual data.
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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