MLS-C01 Data Engineering Practice Question
A team is building a data pipeline that ingests data from an Amazon S3 bucket, transforms it using AWS Glue, and loads it into Amazon Redshift for analysis. The Glue job runs on a schedule every hour. The team has noticed that the job takes longer than expected and sometimes fails due to memory issues. The data volume is variable, with occasional spikes. Which solution should the team implement to optimize the pipeline?
⚠ Common exam trap
A common mix-up: candidates assume increasing job frequency (Option C) will automatically reduce per-run data volume, but without incremental processing (job bookmarks), each run still processes the entire dataset, leading to the same memory issues and higher costs.
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
✓
Enable job bookmarks to process only new data and use a G.2X worker type for more memory.
Enabling job bookmarks allows the Glue job to process only new or changed data since the last run, reducing the data volume per execution. Using the G.2X worker type provides additional memory (e.g., 16 GB per DPU vs. 4 GB for G.1X), which helps prevent out-of-memory failures during data spikes. Together, these optimizations address both the variable data volume and memory constraints without requiring a complete pipeline redesign.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Decrease the number of workers to reduce memory contention.
Why it's wrong here
Fewer workers may cause memory issues to persist.
- ✓
Enable job bookmarks to process only new data and use a G.2X worker type for more memory.
Why this is correct
Job bookmarks prevent reprocessing and larger workers provide more memory.
- ✗
Increase the schedule frequency to run the job more often with smaller data increments.
Why it's wrong here
More frequent runs may increase overhead but not fix memory issues.
- ✗
Replace AWS Glue with Amazon EMR using Spark.
Why it's wrong here
EMR also uses Spark and requires more management.
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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