DEA-C01 Data Ingestion and Transformation Practice Question
A company uses AWS Glue to run ETL jobs that transform data from Amazon S3 (Parquet) into a denormalized format for Amazon Redshift. The Glue job uses the DynamicFrame API. The job is failing with a 'MemoryError' when performing a join operation. The data is skewed on the join key. Which THREE actions can reduce memory usage and improve job stability? (Choose THREE.)
⚠ Common exam trap
Many candidates assume increasing resources (DPUs) or repartitioning will fix memory issues, but they fail to recognize that data skew on the join key is the root cause, which requires skew-aware techniques like salting or broadcast joins.
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 a broadcast join if one of the tables is small enough.
A broadcast join (using `join` with `broadcast` hint or `DynamicFrame.join(..., transformation_ctx='...')` with broadcast enabled) avoids shuffling the larger table across the cluster by copying the small table to every executor. This eliminates the memory pressure from skewed key distribution during the shuffle phase, reducing the risk of a MemoryError.
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 a broadcast join if one of the tables is small enough.
Why this is correct
Avoids shuffling small table.
- ✓
Use a salted join key to distribute skewed keys across partitions.
Why this is correct
Reduces memory pressure from hot keys.
- ✗
Increase the number of DPUs for the Glue job.
Why it's wrong here
General increase may not address skew.
- ✗
Repartition the data on the join key before the join operation.
Why it's wrong here
Does not address skew; may worsen.
- ✓
Split the transformation into multiple Glue job steps to reduce per-step memory.
Why this is correct
Reduces memory per stage.
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
Related to this question
About these practice questions
One of 1,711 original DEA-C01 practice questions on Courseiva, each with a full explanation and wrong-answer analysis — not exam dumps or protected exam content. Learn why practice questions differ from exam dumps →
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.