MLS-C01 Data Engineering Practice Question
A company is using AWS Glue ETL jobs to transform data. The jobs are failing due to insufficient memory. The data processing involves complex joins and aggregations. Which THREE actions can improve job performance and reduce memory usage?
⚠ Common exam trap
Many candidates assume reducing resources (Option B) or eliminating parallelism (Option E) will solve memory issues, when in fact these actions exacerbate the problem by increasing the data load per executor or removing the benefits of distributed processing.
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
✓
Filter and project data early in the transformation to reduce data volume
Filtering and projecting data early in the transformation reduces the volume of data that must be processed in subsequent operations like joins and aggregations. By using pushdown predicates and selecting only necessary columns, you minimize the data shuffled across the cluster, which directly reduces memory pressure and improves job performance in AWS Glue ETL.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Filter and project data early in the transformation to reduce data volume
Why this is correct
Reduces memory footprint.
- ✗
Decrease the number of DPUs allocated to the job
Why it's wrong here
Fewer DPUs mean less memory.
- ✓
Repartition the data and use bucketing to reduce shuffle size
Why this is correct
Reduces data movement across nodes.
- ✓
Increase the number of DPUs (workers) allocated to the job
Why this is correct
More DPUs provide more memory and parallelism.
- ✗
Use a single node cluster to avoid shuffle overhead
Why it's wrong here
Single node may not have enough memory.
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