Quick Answer
The answer is to use a larger worker type like G.2X. This resolves the out of memory error in AWS Glue ETL because the G.1X worker provides only 16 GB of memory per executor, which is insufficient for processing a 500 GB dataset; upgrading to G.2X doubles the memory per worker to 32 GB, giving each Spark executor more heap space to handle larger data partitions without failure. On the AWS Certified Machine Learning Engineer Associate MLA-C01 exam, this scenario tests your understanding of how worker type memory allocation directly impacts job stability during large-scale ETL, often appearing as a trap where candidates mistakenly try to increase the number of workers instead of upgrading the worker type. Remember the memory tip: when facing an out of memory error, think "bigger worker, not more workers" — G.2X doubles per-executor memory, while adding more G.1X workers only increases parallelism, not partition capacity.
This MLA-C01 practice question tests your understanding of data preparation for machine learning. 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 data engineer notices that an AWS Glue ETL job is failing with an Out of Memory error when processing a large dataset. The dataset is 500 GB in size, and the worker type is G.1X. Which change is MOST likely to resolve the issue?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue: "most likely"
Why it matters: Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.
Partition the input data into smaller files
Why wrong: Partitioning helps parallelism but does not address per-worker memory limits.
Use a Spark DataFrame instead of RDD
Why wrong: Glue already uses DataFrames by default.
Increase the number of workers
Why wrong: Adding more workers does not increase memory per worker; the OOM will persist.
Use a larger worker type like G.2X
G.2X provides double the memory of G.1X, resolving the OOM.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
Use a larger worker type like G.2X
The G.1X worker type provides 16 GB of memory per worker. A 500 GB dataset requires sufficient aggregate memory across workers for processing. Increasing the worker type to G.2X (which doubles memory to 32 GB per worker) increases the memory per executor, allowing each task to handle larger data partitions without running out of memory. This directly addresses the Out of Memory error by providing more heap space for Spark operations.
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.
Partition the input data into smaller files
Why it's wrong here
Partitioning helps parallelism but does not address per-worker memory limits.
Use a Spark DataFrame instead of RDD
Why it's wrong here
Glue already uses DataFrames by default.
Increase the number of workers
Why it's wrong here
Adding more workers does not increase memory per worker; the OOM will persist.
Use a larger worker type like G.2X
Why this is correct
G.2X provides double the memory of G.1X, resolving the OOM.
Clue confirmation
The clue word "most likely" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
The trap here is that candidates often assume adding more workers (scaling out) always solves memory issues, but the real bottleneck is per-executor memory, which is only addressed by using a larger worker type (scaling up).
Detailed technical explanation
Under the hood, AWS Glue ETL uses Apache Spark, where each worker runs an executor with a fixed heap size determined by the worker type (G.1X = 16 GB, G.2X = 32 GB). The Out of Memory error typically occurs when a single partition exceeds the executor's memory, often during shuffle operations or when processing wide transformations. In practice, for large datasets, scaling up (larger worker type) is more effective than scaling out (more workers) because it increases the memory ceiling per task, reducing spill to disk and GC overhead.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
A cloud solutions architect for a retail company is evaluating services for a new workload. The correct answer here reflects best practice for the specific scenario described — not a general cloud recommendation. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Cloud exam questions reward reading the constraint carefully: the same technology can be right or wrong depending on the use case.
What to study next
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FAQ
Data Preparation for Machine Learning — This question tests Data Preparation for Machine Learning — Read the scenario before looking for a memorised answer..
The correct answer is: Use a larger worker type like G.2X — The G.1X worker type provides 16 GB of memory per worker. A 500 GB dataset requires sufficient aggregate memory across workers for processing. Increasing the worker type to G.2X (which doubles memory to 32 GB per worker) increases the memory per executor, allowing each task to handle larger data partitions without running out of memory. This directly addresses the Out of Memory error by providing more heap space for Spark operations.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Yes — watch for: "most likely". Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 24, 2026
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