DEA-C01 Data Ingestion and Transformation Practice Question
A company uses AWS Glue ETL jobs to transform data and load it into Amazon Redshift. The jobs are failing with 'Out of Memory' errors. What is the most cost-effective way to resolve this issue without changing the transformation logic?
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 G.1X workers in the Glue job configuration.
Increasing the number of G.1X workers (DPUs) adds parallelism, allowing the job to handle more data in memory without changing logic, and is cost-effective since G.1X workers are cheaper than G.2X. Option B is wrong: Redshift Spectrum is for querying data directly from S3, not for fixing memory issues in Glue ETL jobs. Option C is wrong: Changing to G.2X workers increases memory per worker but is more expensive than adding more G.1X workers; the goal is cost-effective. Option D is wrong: Switching to Scala does not directly address memory issues and may require code changes.
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 G.1X workers in the Glue job configuration.
Why this is correct
More workers increase parallelism and total memory.
- ✗
Use Amazon Redshift Spectrum to query data directly from S3 without transformation.
Why it's wrong here
Redshifting Spectrum bypasses transformation, which may not meet requirements.
- ✗
Change the worker type to G.2X and keep the same number of workers.
Why it's wrong here
Larger workers are more expensive per unit of compute.
- ✗
Switch the job from Python to Scala.
Why it's wrong here
Changing language does not address OOM; Scala may use similar memory.
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
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Variation 1. A company runs a data pipeline using AWS Glue ETL jobs to process daily files from an S3 bucket. The files are in CSV format and range from 1 GB to 10 GB. The Glue job runs successfully for small files but fails with an 'Out of Memory' error for files larger than 5 GB. The job uses a single G.1X DPU (16 GB memory). The company needs to process these large files without changing the existing ETL script. Which solution should the company implement?
hard- A.Convert the input files from CSV to Parquet format to reduce memory usage.
- B.Use the Optimus format in AWS Glue to compress data.
- C.Use Amazon EMR with Spark instead of AWS Glue.
- ✓ D.Increase the number of DPUs and use the G.2X worker type to provide more memory per worker.
Why D: Increasing the number of DPUs and switching to the G.2X worker type allocates more memory per worker (32 GB instead of 16 GB), allowing the Glue job to process larger CSV files without modifying the ETL script. Option A is incorrect because converting to Parquet would require changing the script and may still encounter memory limits with very large files. Option B is incorrect because Optimus format is not a standard AWS Glue feature; the correct approach is to increase memory. Option C is incorrect because moving to Amazon EMR with Spark would likely require rewriting the script, which the company wants to avoid.
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.