- A
Switch the training job to use Spot instances to reduce cost and potentially improve throughput.
Why wrong: Spot instances do not fix performance bottlenecks; they may interrupt jobs.
- B
Increase the number of training instances to parallelize data loading.
Why wrong: Increasing instances can worsen the problem if the bottleneck is not compute-bound.
- C
Stop and restart the training job with a different instance type.
Why wrong: Restarting without root cause analysis may lead to the same issue.
- D
Review CloudWatch Logs for the training container to identify errors or warnings.
Logs often show the exact cause of hanging, such as waiting for data or resource constraints.
Question 900 of 1,000
mediumMultiple ChoiceObjective-mapped
Troubleshooting Stuck SageMaker Training Jobs
This MLA-C01 practice question tests your understanding of training a deep learning model on amazon sagemaker. 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 training a deep learning model on Amazon SageMaker. The training job started but has been stuck in 'InProgress' state for an unusually long time with low CPU utilization. The data scientist suspects a bottleneck. What should be the first troubleshooting step?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue:
"first"Why it matters: Order matters here. You are being tested on which action comes before the others — not which action is generally useful.
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
✓
Review CloudWatch Logs for the training container to identify errors or warnings.
When a SageMaker training job is stuck in 'InProgress' with low CPU utilization, the most common cause is a bottleneck in data loading or preprocessing within the training container. Reviewing CloudWatch Logs for the training container is the first troubleshooting step because it provides direct visibility into container-level errors, warnings, or stalls (e.g., hanging on a file read, waiting for a dependency, or a misconfigured data channel) that would not be visible from instance-level metrics alone.
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.
- ✗
Switch the training job to use Spot instances to reduce cost and potentially improve throughput.
Why it's wrong here
Spot instances do not fix performance bottlenecks; they may interrupt jobs.
- ✗
Increase the number of training instances to parallelize data loading.
Why it's wrong here
Increasing instances can worsen the problem if the bottleneck is not compute-bound.
- ✗
Stop and restart the training job with a different instance type.
Why it's wrong here
Restarting without root cause analysis may lead to the same issue.
- ✓
Review CloudWatch Logs for the training container to identify errors or warnings.
Why this is correct
Logs often show the exact cause of hanging, such as waiting for data or resource constraints.
Clue confirmation
The clue word "first" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often jump to scaling or instance changes (Options B and C) without first checking logs, assuming a performance issue is hardware-related when it is almost always a software or configuration issue inside the container.
Detailed technical explanation
How to think about this question
Under the hood, SageMaker training containers emit stdout/stderr and framework logs to CloudWatch Logs in real time. A common subtle behavior is that a training script may appear to hang due to a deadlock in multi-threaded data loading (e.g., Python's Global Interpreter Lock contention or a misconfigured `num_workers` in PyTorch DataLoader), which manifests as low CPU utilization on the instance but high I/O wait. In a real-world scenario, a data scientist might see the job stuck at 'InProgress' for hours, only to find in CloudWatch Logs that the training script is repeatedly retrying a failed S3 download due to insufficient IAM permissions.
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
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
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.
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FAQ
Questions learners often ask
What does this MLA-C01 question test?
Read the scenario before looking for a memorised answer.
What is the correct answer to this question?
The correct answer is: Review CloudWatch Logs for the training container to identify errors or warnings. — When a SageMaker training job is stuck in 'InProgress' with low CPU utilization, the most common cause is a bottleneck in data loading or preprocessing within the training container. Reviewing CloudWatch Logs for the training container is the first troubleshooting step because it provides direct visibility into container-level errors, warnings, or stalls (e.g., hanging on a file read, waiting for a dependency, or a misconfigured data channel) that would not be visible from instance-level metrics alone.
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.
Are there clue words in this question I should notice?
Yes — watch for: "first". Order matters here. You are being tested on which action comes before the others — not which action is generally useful.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Last reviewed: Jul 4, 2026
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