DEA-C01 Data Ingestion and Transformation Practice Question
A company uses AWS Glue to transform data in an S3 data lake. The transformation logic requires joining two large datasets that are each hundreds of gigabytes. The Glue job runs out of memory. Which configuration change will most likely resolve this issue?
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 for the Glue job.
Increasing the number of DPUs provides more memory for the join operation. Glue automatically distributes data across workers, so more workers mean more total memory.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Repartition the data before the join.
Why it's wrong here
Repartitioning can help with skew but not necessarily with memory for a large join.
- ✓
Increase the number of DPUs for the Glue job.
Why this is correct
More DPUs provide more memory and parallelism, helping the join fit in memory.
- ✗
Use a different file format like Parquet with compression.
Why it's wrong here
File format affects read efficiency but not join memory directly.
- ✗
Use the 'spark.sql.autoBroadcastJoinThreshold' setting to broadcast the smaller table.
Why it's wrong here
This helps if one table is small; both are large, so broadcasting won't help.
Visual reference
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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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 retail company uses AWS Glue to process daily sales data from multiple CSV files stored in Amazon S3. The Glue job runs a PySpark script that reads the files, performs joins, and writes the output as Parquet. Recently, the job has been failing with 'Out of Memory' errors. The data volume has grown from 10 GB to 50 GB per day. The Glue job uses 10 DPUs and the standard worker type. The data engineer needs to fix the job without rewriting the script. What should the data engineer do?
medium- A.Split the input CSV files into smaller partitions.
- ✓ B.Change the worker type to G.2X to get more memory per worker.
- C.Decrease the number of DPUs to reduce memory contention.
- D.Increase the number of DPUs for the Glue job to 20.
Why B: Out of Memory errors in AWS Glue are typically caused by insufficient per-executor memory during operations like joins. Changing the worker type to G.2X doubles the memory per DPU (from 16 GB to 32 GB), directly addressing the OOM issue without rewriting the script. Option D is wrong because increasing the number of DPUs adds more executors but does not increase the memory per executor; it only increases parallelism, which may not resolve OOM if a single executor runs out of memory. Option A (splitting input files) does not reduce the memory footprint of joins. Option C (decreasing DPUs) reduces resources and worsens the problem.
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.