This MLA-C01 practice question tests your understanding of mla-c01 exam topics. 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.
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?
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
}
}
A
Change the algorithm
Why wrong: The algorithm is not the cause; memory insufficiency is hardware-related.
B
Use a larger instance type
A larger instance type has more memory, addressing the out-of-memory error.
C
Increase the volume size
Why wrong: Volume size is for storage, not memory.
D
Increase the instance count
Why wrong: Adding more instances does not increase memory per instance; the job runs on a single instance.
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 message indicates that the training job failed due to insufficient memory (an out-of-memory error). Using a larger instance type (Option B) provides more RAM, which directly resolves the memory exhaustion issue. SageMaker training jobs run on EC2 instances, and the instance type determines the available memory and compute resources.
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
The trap here is that candidates often confuse 'out of memory' with 'out of disk space' and incorrectly choose to increase the volume size (Option C), but the error is specifically about RAM exhaustion, not storage.
Detailed technical explanation
How to think about this question
SageMaker training jobs allocate memory from the instance's RAM, and the error 'OutOfMemoryError' typically occurs when the dataset or model parameters exceed the available heap or physical memory. For deep learning frameworks like TensorFlow or PyTorch, the memory footprint includes model weights, gradients, optimizer states, and activations; switching to a memory-optimized instance (e.g., r5 or p3dn families) or a compute-optimized instance with higher RAM (e.g., c5.18xlarge) can resolve this. In practice, monitoring CloudWatch metrics like 'mem_used_percent' during training helps identify memory pressure before failure.
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.
TExam Day Tips
→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
An e-commerce site experiences heavy traffic on Black Friday and near-zero traffic during off-peak weeks. Rather than provisioning permanent large VMs, the team uses auto-scaling groups that add capacity automatically under load and reduce it overnight. Questions like this test whether you understand elasticity, availability zones, and cloud compute scaling patterns.
Visual reference
What to study next
Got this wrong? Here's your next step.
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.
What is the correct answer to this question?
The correct answer is: Use a larger instance type — The error message indicates that the training job failed due to insufficient memory (an out-of-memory error). Using a larger instance type (Option B) provides more RAM, which directly resolves the memory exhaustion issue. SageMaker training jobs run on EC2 instances, and the instance type determines the available memory and compute resources.
What should I do if I get this MLA-C01 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
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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