DEA-C01 Data Ingestion and Transformation Practice Question
A data engineering team uses AWS Glue ETL jobs to process data daily. They notice that job run times are increasing as data volume grows. Which action will most effectively improve performance without changing the code?
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
✓
Increase the number of DPUs for the Glue job.
Increasing the number of DPUs (Data Processing Units) allocated to the job provides more parallelism and memory, speeding up processing without code changes.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Use a smaller instance type for the Glue job.
Why it's wrong here
Smaller instances reduce resources, making performance worse.
- ✗
Enable job bookmark to skip previously processed data.
Why it's wrong here
Job bookmarks help with incremental processing but won't improve performance of full loads.
- ✗
Split the data into more files in S3.
Why it's wrong here
While file splitting can help parallelism, it's a data layout change, not a direct performance tuning.
- ✓
Increase the number of DPUs for the Glue job.
Why this is correct
More DPUs increase parallelism and can significantly reduce run time.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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