The answer is to use a larger instance type, such as moving from ml.m5.large to ml.m5.xlarge. This is correct because the error message indicates the training job ran out of memory on the current instance, meaning the model or dataset exceeds the available RAM. In SageMaker, instance memory is fixed per instance type, so scaling up to a larger instance provides more memory per node, whereas increasing instance count only distributes the workload across multiple nodes without increasing per-instance memory. On the AWS Certified Machine Learning Engineer Associate MLA-C01 exam, this question tests your understanding of SageMaker resource management and common failure modes—a frequent trap is confusing horizontal scaling (adding instances) with vertical scaling (larger instances). Remember: for memory errors, think “bigger box, not more boxes.” A useful mnemonic is “RAM up, not out”—when you see an out-of-memory error, scale up the instance type, not the instance count.
MLA-C01 ML Model Development Practice Question
This MLA-C01 practice question tests your understanding of ml model development. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. 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.
Exhibit
{
"TrainingJobName": "job-123",
"TrainingJobStatus": "Failed",
"FailureReason": "ClientError: Review the error message. Training failed due to insufficient instance memory.",
"AlgorithmSpecification": {
"TrainingImage": "123456789012.dkr.ecr.us-east-1.amazonaws.com/sagemaker-xgboost:1.0-1",
"TrainingInputMode": "File"
},
"ResourceConfig": {
"InstanceType": "ml.m5.large",
"InstanceCount": 1,
"VolumeSizeInGB": 30
}
}
Refer to the exhibit. A SageMaker training job failed. Based on the error message, which action should the engineer take?
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 instance type
The error indicates insufficient instance memory. The ml.m5.large instance has limited memory; using a larger instance type (e.g., ml.m5.xlarge) provides more memory. Increasing instance count would distribute but not increase per-instance memory; volume size affects storage, not RAM; changing the algorithm may not help.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
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 algorithm
Why it's wrong here
The algorithm is not the cause; memory insufficiency is hardware-related.
✓
Use a larger instance type
Why this is correct
A larger instance type has more memory, addressing the out-of-memory error.
Related concept
Read the scenario before looking for a memorised answer.
✗
Increase the volume size
Why it's wrong here
Volume size is for storage, not memory.
✗
Increase the instance count
Why it's wrong here
Adding more instances does not increase memory per instance; the job runs on a single instance.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Many certification questions include familiar terms but test a specific constraint. Read the exact wording before choosing an answer that is generally true but wrong for this case.
Detailed technical explanation
How to think about this question
This question should be treated as a scenario, not a definition check. Identify the problem, the constraint and the best action. Then compare each option against those facts.
KKey Concepts to Remember
Read the scenario before looking for a memorised answer.
Find the constraint that changes the correct option.
Eliminate answers that are true in general but not in this case.
Use explanations to understand the rule behind the answer.
TExam Day Tips
→Underline the problem statement mentally.
→Watch for words such as best, first, most likely and least administrative effort.
→Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
A media company stores terabytes of video archives that are accessed once a year for audit purposes. Moving these objects to a cold storage tier (Azure Archive, S3 Glacier, or Google Nearline) costs a fraction of hot storage. Questions like this test whether you understand storage tiers, access frequency tradeoffs, and retrieval latency requirements.
What to study next
Got this wrong? Here's your next step.
Identify which MLA-C01 exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
ML Model Development — This question tests ML Model Development — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Use a larger instance type — The error indicates insufficient instance memory. The ml.m5.large instance has limited memory; using a larger instance type (e.g., ml.m5.xlarge) provides more memory. Increasing instance count would distribute but not increase per-instance memory; volume size affects storage, not RAM; changing the algorithm may not help.
What should I do if I get this MLA-C01 question wrong?
Identify which MLA-C01 exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Question Discussion
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