DEA-C01 Data Ingestion and Transformation Practice Question
A data engineer is troubleshooting a slow AWS Glue ETL job that reads from Amazon S3 and writes to Amazon Redshift. The job processes 10 GB of CSV data. The engineer notices that the job runs with a single DPU and takes longer than expected. Which change would MOST likely improve performance?
⚠ Common exam trap
The trap here is that candidates often focus on data format optimization (Parquet) or query engine changes (Redshift Spectrum) without realizing that the primary bottleneck is the single-DPU configuration limiting parallelism, which is a common oversight in Glue job tuning questions.
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 and configure the job to use the S3 list implementation for parallel reads.
The job runs with a single DPU, which limits parallelism. Increasing the number of DPUs allows the job to process data in parallel across multiple workers. Additionally, configuring the S3 list implementation enables the job to list objects in parallel, reducing the overhead of discovering input files. This combination directly addresses the bottleneck of a single-threaded read from S3.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Replace Redshift with Amazon Redshift Spectrum.
Why it's wrong here
Spectrum queries data in S3, but the job is ETL, not ad-hoc querying.
- ✗
Change the input format to Parquet and enable predicate pushdown.
Why it's wrong here
While Parquet can help, the primary bottleneck is likely the single DPU.
- ✗
Use a JDBC connection to read data directly from S3.
Why it's wrong here
JDBC is for databases, not S3.
- ✓
Increase the number of DPUs and configure the job to use the S3 list implementation for parallel reads.
Why this is correct
More DPUs allow parallel processing, and S3 list implementation improves file discovery.
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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Same concept, more angles
1 more way this is tested on DEA-C01
These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.
Variation 1. 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?
hard- A.Use S3 Select to push down filters
- B.Reduce the batch size in the DynamoDB connector
- ✓ C.Increase the number of DPUs
- D.Change output to JSON format to reduce overhead
Why C: 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.
JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This DEA-C01 practice question is part of Courseiva's free Amazon Web Services certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the DEA-C01 exam.