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MLA-C01 Practice Question: A machine learning engineer is using AWS Glue ETL…
A machine learning engineer is using AWS Glue ETL to transform a large dataset stored in Amazon S3. The transformation involves joining two tables on a high-cardinality column and aggregating results. The job is running slowly and the engineer needs to improve performance. Which optimization technique should the engineer apply?
⚠ Common exam trap
MLA-C01 often tests the misconception that throwing more workers at a slow Glue job solves performance problems, when the real issue is shuffle-heavy joins that require bucketing or 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 bucketing on the join key for both tables
Bucketing on the join key for both tables co-locates rows with the same key into the same bucket files, which allows Spark to perform a bucketed join that avoids a full shuffle of the large dataset. This dramatically reduces network I/O and speeds up joins on high-cardinality columns.
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 number of workers and memory allocation
Why it's wrong here
Adding workers and memory scales the cluster but does not address the skewed shuffle that a high-cardinality join creates, so straggler tasks persist. It is tempting because under-provisioned clusters do slow jobs, and this would be correct for genuinely resource-starved workloads.
- ✓
Use bucketing on the join key for both tables
Why this is correct
Bucketing partitions both tables by hash of the join key into a fixed number of buckets, so matching keys land in the same bucket. This lets Glue process each bucket pair independently, avoiding a full shuffle of the high-cardinality column and cutting join and aggregation time.
- ✗
Use Spark SQL instead of the DynamicFrame API
Why it's wrong here
Spark SQL and the DynamicFrame API both compile to the same Spark execution engine, so swapping APIs leaves the shuffle cost of the high-cardinality join unchanged. It is tempting because SQL feels declarative, but the correct fix targets the shuffle itself, such as salting the join key.
- ✗
Convert the job to use Python Shell
Why it's wrong here
Python Shell jobs run single-threaded on one instance and cannot distribute a high-cardinality join or aggregation across a cluster, so the job stays slow. It is tempting for lightweight scripts and small datasets, where a full Spark cluster is unnecessary overhead.
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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