MLS-C01 Data Engineering Practice Question
Network Topology
Refer to the exhibit. A data engineer has deployed this CloudFormation template. The Glue job 'my-etl-job' reads from the S3 bucket 'my-data-lake-bucket' and writes transformed data to another bucket. After 30 days, the data engineer notices that the Glue job fails with 'Input data not found' errors. What is the most likely cause?
⚠ Common exam trap
A common trap on the AWS Machine Learning Specialty exam is that candidates mistakenly attribute failures to job bookmarks or temporary directories instead of recognizing that the lifecycle rule is deleting the source data after the specified retention period.
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
✓
The lifecycle configuration deletes objects from the bucket after 30 days, removing the input data.
The lifecycle configuration on the S3 bucket 'my-data-lake-bucket' is set to delete objects after 30 days. Since the Glue job 'my-etl-job' reads input data from this bucket, once the 30-day period elapses, the input data is removed, causing the 'Input data not found' error. This matches the symptom of the job failing after exactly 30 days.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
The temporary directory 'my-temp-dir' is being cleaned up by the lifecycle configuration.
Why it's wrong here
The temp directory is not in the same bucket; it's in a different bucket.
- ✗
The script location 's3://my-scripts/etl.py' is being deleted by the lifecycle rule.
Why it's wrong here
The script is in a different bucket, not affected by this lifecycle rule.
- ✗
The job bookmark option 'job-bookmark-enable' is causing the job to skip newly arriving data.
Why it's wrong here
Job bookmarks track processed data but do not cause data loss.
- ✓
The lifecycle configuration deletes objects from the bucket after 30 days, removing the input data.
Why this is correct
The ExpirationInDays: 30 rule deletes objects older than 30 days, which may include input 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 |
Go deeper
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