DEA-C01 Data Ingestion and Transformation Practice Question
A data engineering team is troubleshooting a slow AWS Glue ETL job that reads from an Amazon DynamoDB table and writes to Amazon S3 in Parquet format. The job processes 50 GB of data. Which action would most effectively improve job performance?
⚠ Common exam trap
Candidates often confuse S3 Select as a universal filter mechanism or assume that reducing batch size always improves performance, when in fact it increases API overhead and latency in distributed systems like Glue.
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
Increasing the number of DPUs (Data Processing Units) for the AWS Glue job directly allocates more distributed computing resources (CPU, memory, and network bandwidth) to parallelize the read from DynamoDB and the write to S3. Since the job processes 50 GB of data, the bottleneck is likely the throughput of the Glue Spark cluster, and adding DPUs increases parallelism, reducing overall execution time.
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 S3 Select to push down filters
Why it's wrong here
S3 Select pushes filters down to S3 reads, but this job reads from DynamoDB and writes to S3, so no S3 scan is being filtered. It is correct when a job reads large CSV or JSON objects from S3 and needs only a subset of rows or columns.
- ✗
Reduce the batch size in the DynamoDB connector
Why it's wrong here
Reducing the DynamoDB connector batch size lowers the number of items read per request, increasing round trips and slowing the job further. It is appropriate when throttling or memory pressure occurs, not when the goal is maximising read throughput across 50 GB.
- ✓
Increase the number of DPUs
Why this is correct
Adding DPUs scales the number of Apache Spark executors, giving more parallel workers to read the 50 GB DynamoDB table and write Parquet to S3. This directly addresses the compute-bound bottleneck, unlike tuning partition counts or connection settings.
- ✗
Change output to JSON format to reduce overhead
Why it's wrong here
Switching to JSON would increase file size and I/O overhead compared to Parquet’s columnar compression and predicate pushdown, directly worsening the bottleneck when writing 50 GB to S3. This choice is tempting because JSON is human-readable and schema-flexible, making it ideal for streaming logs or semi-structured data where schema evolution is frequent and compression is not the primary concern.
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 by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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