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.)
⚠ Common exam trap
DEA-C01 often tests the misconception that more transformations or disabling compression improve ETL speed, when in fact pushdown predicates and additional DPUs are the canonical performance levers.
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.
Option B is correct because pushdown predicates let AWS Glue push filtering logic down to the source RDS for MySQL database, so only the required incremental rows are read over JDBC instead of the entire table, reducing I/O and shuffle work in the job. Option D is correct because increasing the number of DPUs adds more Apache Spark executors and parallel task slots, which improves throughput for a large, daily incremental ETL workload that is currently resource-bound. Option A is not appropriate because switching to a Standard single-node worker removes distributed processing and would generally slow the job rather than improve performance. Option C is not appropriate because adding more transformations increases CPU and memory work in the ETL script, which would make the job slower, not faster. Option E is not appropriate because disabling output compression increases the volume of data written to Amazon S3 and read downstream, raising I/O and cost rather than improving job 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 translate filter conditions into SQL WHERE clauses executed by RDS for MySQL, so only matching incremental rows are read over JDBC. Less data is transferred and processed in Glue, directly reducing the job's runtime.
- ✗
Add more transformations to the ETL script to clean data.
Why it's wrong here
Additional cleaning transformations add per-row compute and shuffle stages to an already slow job, lengthening runtime. It is tempting because data quality matters, but transformation count is not the performance axis here; reducing data scanned and increasing parallelism are.
- ✓
Increase the number of DPUs for the Glue job.
Why this is correct
Increasing DPUs allocates more Apache Spark executors and cores to the Glue job, enabling greater parallel processing of partitions during the extract, transform, and load stages. This directly shortens runtime for the daily incremental workload.
- ✗
Disable compression on the output data to reduce CPU usage.
Why it's wrong here
Disabling compression increases output bytes written to Amazon S3, raising I/O and network time rather than reducing it; CPU spent compressing is offset by smaller writes. Compression would be the wrong lever only if CPU were the proven bottleneck, which the stem does not indicate.
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 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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