MLS-C01 Data Engineering Practice Question
Network Topology
A data engineer is investigating a slow Athena query on a partitioned table. The table is partitioned by year, month, and day, and the data is stored in S3 with the prefix pattern 'raw/YYYY/MM/DD/'. The engineer runs the above CLI command and sees that there are many small files. Which action would most improve query performance?
⚠ Common exam trap
Many candidates confuse the small-files problem with data format optimization, choosing Parquet/ORC (Option A) because they know columnar formats are faster, but they miss that the primary bottleneck is file count, not encoding.
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 S3DistCp to coalesce files into fewer, larger files.
The core issue is that the Athena query is slow due to many small files, which increases the overhead of S3 LIST operations and task scheduling in the Presto/Trino engine underlying Athena. Coalescing these small files into fewer, larger files with S3DistCp reduces the number of S3 GET requests and minimizes the scheduling overhead, directly improving query throughput. This is a classic small-files problem, not a data format or partitioning issue.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Convert the data to columnar format like Parquet or ORC.
Why it's wrong here
Columnar formats help but do not directly address the small files issue.
- ✓
Use S3DistCp to coalesce files into fewer, larger files.
Why this is correct
Coalescing reduces the number of files, improving query performance.
- ✗
Increase the number of partitions in the Athena DDL.
Why it's wrong here
More partitions without fixing the file size issue may not help.
- ✗
Add more partitions to reduce the amount of data scanned per query.
Why it's wrong here
More partitions can increase overhead if there are many small files.
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 by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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