DEA-C01 Data Ingestion and Transformation Practice Question
Exhibit
Refer to the exhibit. Error log from AWS Glue job: ``` An error occurred while calling o123.pyWriteDynamicFrame. org.apache.spark.SparkException: Job aborted due to stage failure: Task 0 in stage 4.0 failed 4 times, most recent failure: Lost task 0.3 in stage 4.0 (TID 8, ip-10-0-1-45.ec2.internal): java.lang.OutOfMemoryError: Java heap space ```
A data engineer runs an AWS Glue ETL job that reads from a large Amazon S3 source (several terabytes of CSV files) and writes transformed data to an S3 bucket in Parquet format. The job fails with the error shown in the exhibit. The job uses the Standard worker type with 10 workers (G.1X). The engineer needs to resolve the failure with minimal cost increase. What should the engineer do?
⚠ Common exam trap
It's easy for candidates to assume scaling out (more workers) is always the cheapest fix, but increasing worker memory (scaling up) is often more cost-effective for memory-bound shuffle operations because it avoids the overhead of additional task serialization and network shuffling.
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
✓
Change the worker type to G.2X with 10 workers.
The error indicates that the Glue job is running out of memory during the shuffle phase, which is common when processing large datasets with transformations that require data redistribution. Changing the worker type to G.2X doubles the memory per worker (from 16 GB to 32 GB) without increasing the number of workers, providing the necessary memory headroom for the shuffle operation at a minimal cost increase compared to scaling out with more workers.
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 workers to 20 while keeping G.1X worker type.
Why it's wrong here
More workers spread the workload but each executor still has limited memory; the OOM may still occur.
- ✓
Change the worker type to G.2X with 10 workers.
Why this is correct
G.2X provides double the memory (32 GB) per worker compared to G.1X (16 GB), resolving the heap space error with minimal cost increase.
- ✗
Change the worker type to G.4X with 10 workers.
Why it's wrong here
G.4X provides 4 vCPUs and 16 GB memory (same as G.1X); it does not increase memory per executor.
- ✗
Set the 'coalesce' parameter to reduce the number of output files.
Why it's wrong here
Coalesce reduces partitions but does not increase executor memory; it might even cause more OOM if partitions are combined.
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 data pipeline uses AWS Glue to process large CSV files. The team notices that some jobs fail with out-of-memory errors. Which TWO configuration changes can help mitigate this issue?
hard- A.Reduce the number of DPUs to limit concurrency.
- ✓ B.Increase the number of DPUs for the Glue job.
- ✓ C.Enable Glue job autoscaling.
- D.Convert input files from CSV to Parquet.
- E.Enable job bookmarks.
Why B: Options B and C are correct: increasing the number of DPUs provides more memory, and enabling autoscaling allows the job to automatically scale resources as needed. Option A (reducing DPUs) would worsen the problem by limiting resources. Option D (converting to Parquet) can improve performance but is not a direct configuration change for the Glue job itself. Option E (job bookmarks) is for incremental processing and does not affect memory.
JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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