AWS Glue Insufficient DPU: How to Fix Memory and Timeout Errors
A company is using AWS Glue to run ETL jobs that process data in an S3 data lake. The jobs are failing with out-of-memory errors when processing large files. Which configuration change should be made to resolve this issue?
Quick Answer
The answer is to increase the number of DPUs allocated to the job. This is the correct configuration change because AWS Glue uses Data Processing Units (DPUs) as a measure of both compute and memory; each DPU provides 4 vCPUs and 16 GB of memory, so increasing the DPU count directly expands the total memory available to the ETL process, preventing out-of-memory errors when handling large files in your S3 data lake. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this question tests your understanding of Glue job resource tuning, often appearing as a distractor where candidates confuse partitioning or job bookmarks with memory fixes—remember that partitioning helps with parallelism, not per-task memory limits. A common trap is choosing a larger worker type like G.2X, but the simpler and more direct fix for memory errors is simply raising the DPU count on the default Standard worker. Memory tip: "More DPUs, more juice"—when Glue jobs choke on big data, pump up the DPUs.
⚠ Common exam trap
It's easy for candidates to confuse scaling vertically (changing worker type) with scaling horizontally (adding DPUs), but for large files, increasing DPUs is the more effective and direct solution for out-of-memory errors in AWS Glue.
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 allocated to the job
Out-of-memory errors in AWS Glue ETL jobs indicate that the allocated memory (DPUs) is insufficient for the data being processed. Increasing the number of DPUs allocates more memory and compute capacity to the job, directly resolving the memory constraint. This is the standard approach for scaling Glue jobs handling large datasets.
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 worker type to G.1X
Why it's wrong here
G.1X has 4 vCPU and 16 GB memory; G.2X (8 vCPU, 32 GB) would be better, but increasing DPUs is the direct fix.
- ✓
Increase the number of DPUs allocated to the job
Why this is correct
More DPUs provide more memory and compute resources.
- ✗
Partition the input data into smaller files
Why it's wrong here
Partitioning helps with parallelism but does not increase memory per job.
- ✗
Enable job bookmark to process only new data
Why it's wrong here
Job bookmarks are for incremental processing, not 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
2 more ways this is tested on MLS-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 company uses AWS Glue ETL jobs to process data from multiple sources. The job fails with the error: 'An error occurred while calling o123.pyWriteDynamicFrame. Insufficient memory.' The job runs on a G.1X worker type with 10 workers. What should be changed to resolve this error?
medium- ✓ A.Increase the number of workers to 20.
- B.Enable the Spark UI to monitor the job.
- C.Change the worker type to G.2X.
- D.Reduce the number of partitions in the DynamicFrame.
Why A: The error 'Insufficient memory' in AWS Glue ETL jobs typically indicates that the total memory across all executors is insufficient for the data being processed. Increasing the number of workers from 10 to 20 doubles the total memory and compute capacity available, allowing the job to handle larger datasets without running out of memory. This is the most direct and effective fix for a memory exhaustion error when using the G.1X worker type.
Variation 2. A company is using AWS Glue to run ETL jobs that transform data from Amazon S3 to Amazon Redshift. The jobs are failing intermittently with timeouts. What is the most likely cause?
easy- A.The S3 bucket policy is too restrictive.
- ✓ B.The AWS Glue job does not have enough DPUs (Data Processing Units) allocated.
- C.The Amazon Redshift cluster is in maintenance mode.
- D.The source data is not compressed.
Why B: Intermittent timeouts in AWS Glue ETL jobs typically indicate insufficient resource allocation. DPUs (Data Processing Units) define the compute capacity for the job; if too few are allocated, the job may run slowly and exceed the default timeout (e.g., 2880 minutes) or internal service limits, especially when processing large datasets from S3 to Redshift. Increasing the DPU count or using the G.1X/G.2X worker types can resolve this.
JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This MLS-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 MLS-C01 exam.