DEA-C01 Data Ingestion and Transformation Practice Question
A company uses AWS Glue to process CSV files stored in Amazon S3. The data pipeline runs daily, but recently some jobs have failed with a 'MemoryError'. The data volume has grown from 1 GB to 10 GB per day. What is the MOST cost-effective solution to 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 (Data Processing Units) allocated to the Glue job.
Increasing the number of DPUs allocates more memory and processing capacity to the Glue job, directly addressing the MemoryError caused by data growth from 1 GB to 10 GB. Option A is wrong because changing the worker type to G.2X provides more memory per worker, but it is a more expensive option compared to simply increasing DPUs, which scales horizontally. Option C is wrong because converting CSV to Parquet improves performance and reduces storage but does not add memory to the Glue job; the job still runs with the same DPU allocation. Option D is wrong because migrating to Amazon EMR introduces additional operational complexity and cost; increasing DPUs in Glue is simpler and more cost-effective for scaling an existing job.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Change the Glue worker type from Standard to G.2X.
Why it's wrong here
G.2X is for GPU workloads, not for general memory increase.
- ✓
Increase the number of DPUs (Data Processing Units) allocated to the Glue job.
Why this is correct
More DPUs provide more memory and processing power.
- ✗
Convert the CSV files to Parquet format using an S3 batch operation.
Why it's wrong here
Changing file format does not increase memory allocation.
- ✗
Migrate the job to Amazon EMR with a larger cluster.
Why it's wrong here
Migrating to Amazon EMR with a larger cluster introduces significant overhead for cluster provisioning, management, and per-hour compute costs, whereas the memory error in a single-node AWS Glue job can be resolved by simply increasing the number of DPUs or switching to the AWS Glue G.1X or G.2X worker type, which directly expands available memory without the operational complexity of a distributed cluster. This option is tempting because EMR is designed for large-scale, long-running data processing tasks that require custom cluster configurations, and it would be the correct choice if the workload demanded distributed processing across hundreds of gigabytes or needed tight integration with custom Spark or Hadoop libraries that Glue cannot natively support.
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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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.