DEA-C01 Data Ingestion and Transformation Practice Question
A data engineering team uses AWS Glue to extract, transform, and load (ETL) data from Amazon RDS for MySQL to Amazon S3. The job runs daily and processes incremental data. The team notices that the job is taking longer than expected. Which TWO actions can improve the job performance? (Choose two.)
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 pushdown predicates to filter data at the source.
B is correct because pushdown predicates allow filtering at the source (RDS MySQL), reducing the amount of data transferred to the Glue job and thus speeding up processing. D is correct because increasing DPUs allocates more resources (CPU, memory) to the Glue job, enabling parallel processing and faster execution. A is wrong because changing to Standard (single node) reduces parallelism, slowing down the job. C is wrong because adding more transformations increases processing time, not improving performance. E is wrong because disabling compression on output data increases I/O and storage costs, not improving performance.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Change the worker type to Standard (single node).
Why it's wrong here
Changing to Standard (single node) reduces parallelism, which would slow down the job.
- ✓
Use pushdown predicates to filter data at the source.
Why this is correct
Pushdown predicates filter data at the source, reducing data transfer and improving performance.
- ✗
Add more transformations to the ETL script to clean data.
Why it's wrong here
Adding more transformations increases processing time, not improves performance.
- ✓
Increase the number of DPUs for the Glue job.
Why this is correct
Increasing DPUs allocates more resources, enabling parallel processing and faster execution.
- ✗
Disable compression on the output data to reduce CPU usage.
Why it's wrong here
Disabling compression increases I/O and storage costs, not improving performance.
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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