Quick Answer
The correct approach is to increase the number of workers in the AWS Glue job configuration. This resolves out of memory errors by distributing the data processing load across more Spark executors, which increases the aggregate memory available without requiring a more expensive worker type. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this question tests your understanding of horizontal scaling versus vertical scaling in serverless ETL environments—a common trap is choosing to upgrade to a larger worker type, which is often less cost-effective. Remember that Glue bills per DPU per second, so adding workers scales memory linearly while keeping per-worker costs the same. A useful memory tip: "More workers, more memory—not bigger workers."
This MLS-C01 practice question tests your understanding of machine learning implementation and operations. The scenario asks you to isolate a root cause — eliminate options that address a different problem before choosing. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
A company is using AWS Glue to run ETL jobs that transform data for machine learning. The jobs are failing with 'Out of Memory' errors. The data size is growing, and the company needs a cost-effective solution. Which approach should be taken?
Switch to Spark on Amazon EMR.
Why wrong: Higher cost and operational overhead.
Increase the number of workers in the job configuration.
Increases parallelism, reducing memory per worker.
Optimize the job by filtering data earlier.
Why wrong: May help but not always sufficient.
Use a larger worker type like G.2X.
Why wrong: Increases cost per worker.
Answer choices
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 workers in the job configuration.
Increasing the number of workers in the AWS Glue job configuration distributes the data processing load across more Spark executors, directly addressing the 'Out of Memory' error by providing more aggregate memory without changing the worker type. This is a cost-effective approach because it scales horizontally, often at a lower cost than moving to a larger worker type, and it leverages the existing Glue infrastructure without migrating to EMR.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
For each option: why learners choose it and why it is or isn't the right answer here.
Switch to Spark on Amazon EMR.
Why it's wrong here
Higher cost and operational overhead.
Increase the number of workers in the job configuration.
Why this is correct
Increases parallelism, reducing memory per worker.
Related concept
Read the scenario before looking for a memorised answer.
Optimize the job by filtering data earlier.
Why it's wrong here
May help but not always sufficient.
Use a larger worker type like G.2X.
Why it's wrong here
Increases cost per worker.
Common exam traps
The trap here is that candidates often assume 'Out of Memory' errors must be solved by increasing memory per worker (vertical scaling) or by switching to a more powerful service, but the most cost-effective and direct solution in AWS Glue is to increase the number of workers (horizontal scaling) to distribute the memory load.
Detailed technical explanation
AWS Glue runs on Apache Spark, where memory errors often occur when the total executor memory is insufficient for the data shuffle or processing stages. Increasing the number of workers adds more Spark executors, each with its own memory allocation, effectively increasing the cluster's total memory and parallelism. In practice, horizontal scaling (adding workers) is often more cost-effective than vertical scaling (larger worker types) because Glue charges per DPU (Data Processing Unit) per second, and G.2X workers consume 2 DPUs each, making them more expensive per worker than standard G.1X workers.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
A startup's cloud architect reviews their monthly bill and notices costs are higher than expected for a long-running batch job. Switching from on-demand instances to Reserved Instances — or using Spot/Preemptible VMs — can reduce compute costs by up to 72 %. Questions like this test whether you understand the tradeoffs between commitment, flexibility, and cost across cloud pricing models.
What to study next
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FAQ
Machine Learning Implementation and Operations — This question tests Machine Learning Implementation and Operations — Read the scenario before looking for a memorised answer..
The correct answer is: Increase the number of workers in the job configuration. — Increasing the number of workers in the AWS Glue job configuration distributes the data processing load across more Spark executors, directly addressing the 'Out of Memory' error by providing more aggregate memory without changing the worker type. This is a cost-effective approach because it scales horizontally, often at a lower cost than moving to a larger worker type, and it leverages the existing Glue infrastructure without migrating to EMR.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 11, 2026
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.
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