MLS-C01 Data Engineering Practice Question
A team is building a data lake on Amazon S3 and using AWS Glue to catalog data. They notice that Glue crawlers are taking too long to update the catalog for a large dataset with millions of small files. Which approach will MOST improve crawler performance?
⚠ Common exam trap
Many candidates confuse partitioning (which improves query pruning) with file consolidation (which reduces metadata and I/O overhead), leading them to select partitioning as a performance fix for crawlers when it does not address the root cause of high file count.
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
✓
Consolidate the small files into larger files (e.g., 100 MB each).
AWS Glue crawlers incur significant overhead when processing millions of small files because each file requires a separate read, schema inference, and metadata write operation. Consolidating small files into larger files (e.g., 100 MB each) reduces the total number of objects that the crawler must scan, dramatically decreasing the time spent on file-level operations and improving overall throughput.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Increase the frequency of the crawler runs.
Why it's wrong here
More frequent runs don't reduce the time per run.
- ✓
Consolidate the small files into larger files (e.g., 100 MB each).
Why this is correct
Fewer, larger files reduce overhead and crawler scan time.
- ✗
Partition the data by date in S3.
Why it's wrong here
Partitioning helps but each partition may still contain many small files.
- ✗
Use a custom classifier to parse the data.
Why it's wrong here
Classifiers determine schema but don't reduce scan time.
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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