DEA-C01 Data Ingestion and Transformation Practice Question
A data engineer is using AWS Glue to process a large dataset stored in Amazon S3. The dataset is partitioned by year/month/day and consists of Parquet files. The engineer notices that the Glue job is running slowly and consuming excessive DPU hours. The job performs a join between two large tables and writes the output back to S3. Which optimization technique should the engineer implement to improve performance and reduce cost?
⚠ Common exam trap
The trap here is thinking that simply adding more DPUs will solve performance issues without addressing the underlying data movement and join inefficiencies.
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 predicate pushdown to filter data before the join and partition the output by a common column.
Predicate pushdown reduces the amount of data read and shuffled by applying filters early in the execution plan. Partitioning the output by a common column improves data layout for subsequent queries and reduces write overhead. Together, these techniques optimize join performance and lower DPU consumption, directly addressing the slow job and high cost. Other options either do not target the join inefficiency or would worsen 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.
- ✗
Increase the number of DPUs allocated to the job to speed up execution.
Why it's wrong here
Increasing DPUs can speed up execution but also increases cost, which conflicts with the requirement to reduce DPU hours. It does not address the underlying inefficiency of the join or data shuffling. Without optimizing the join and data layout, simply adding more resources may not yield proportional performance gains and could result in higher costs. This is a brute-force approach rather than an optimization.
- ✓
Use predicate pushdown to filter data before the join and partition the output by a common column.
Why this is correct
Predicate pushdown filters data at the source, reducing the amount of data read and shuffled during the join. Partitioning the output by a common column improves downstream query performance and reduces write overhead. These techniques directly address the slow join and high DPU usage by minimizing data movement and processing. This is a standard optimization for Glue ETL jobs dealing with large partitioned datasets.
- ✗
Convert the Parquet files to CSV to reduce storage size and improve read performance.
Why it's wrong here
Parquet is a columnar format that is more efficient for analytics than CSV, offering better compression and predicate pushdown capabilities. Converting to CSV would increase storage size and degrade read performance, as CSV is row-based and lacks schema enforcement. This would likely worsen the job's performance and increase DPU usage, making it counterproductive to the goal of optimization.
- ✗
Enable AWS Glue job bookmarks to avoid reprocessing old data.
Why it's wrong here
Job bookmarks help avoid reprocessing previously processed data, which is useful for incremental jobs, but they do not address the performance of a join between two large tables. Since the dataset is already partitioned and the job likely processes all partitions each run, bookmarks would not reduce the computational load of the join itself. This option does not tackle the core issue of inefficient join execution and excessive DPU consumption.
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 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
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.