DEA-C01 Data Ingestion and Transformation Practice Question
Which TWO practices improve the performance of AWS Glue ETL jobs? (Choose two.)
⚠ Common exam trap
Many exam-takers confuse monitoring features (like enabling metrics and logging) with performance optimizations, or mistakenly believe that maximizing resources (DPUs) always improves speed, ignoring the overhead of small files and the benefits of early filtering and column selection.
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
Pushdown predicates (Option A) improve AWS Glue ETL performance by filtering data at the source before it is read into the job. This reduces the volume of data transferred and processed, which is especially effective when using formats like Parquet or ORC that support predicate pushdown natively. By applying filters early, Glue avoids scanning unnecessary partitions or rows, leading to faster execution and lower costs.
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 pushdown predicates to filter data at the source
Why this is correct
Filters data early, reducing data scanned.
- ✗
Increase the number of DPUs to the maximum allowed
Why it's wrong here
More DPUs than needed can cause resource contention and overhead.
- ✗
Use the smallest possible file size for input data
Why it's wrong here
Many small files increase processing overhead.
- ✗
Enable AWS Glue job metrics and debug logging
Why it's wrong here
Debug logging adds overhead; metrics are for monitoring, not performance.
- ✓
Use column pruning to select only required columns
Why this is correct
Reduces data volume processed and shuffled.
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Same concept, more angles
1 more way this is tested on DEA-C01
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Variation 1. An e-commerce company uses AWS Glue to run ETL jobs that transform clickstream data from Amazon S3. The job reads Parquet files, performs aggregations, and writes the results to Amazon Redshift. The job runs successfully but takes longer than expected. The data volume is increasing. Which design change would MOST improve the job's performance?
hard- A.Write the aggregated results to a single large file instead of multiple partitions.
- B.Convert the Parquet files to CSV to simplify the schema.
- C.Replace the Redshift target with Amazon Redshift Spectrum.
- ✓ D.Increase the number of Glue worker nodes (DPUs) for the job.
Why D: Increasing the number of Glue worker nodes (DPUs) directly scales the distributed processing capacity of the ETL job, allowing it to process larger volumes of Parquet data in parallel. This is the most straightforward way to reduce execution time when data volume is growing, as AWS Glue automatically partitions the workload across the additional workers.
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.