- A
Retry the training job with the same configuration; the error might be transient.
Why wrong: Retrying without diagnosis will likely fail again.
- B
Use the SageMaker Debugger to capture system metrics and output tensors for analysis.
Debugger can capture detailed metrics that help identify why the container exited.
- C
Check the CloudWatch Logs for the training job to see the container's stdout and stderr.
CloudWatch logs capture the container's output, which often contains error details.
- D
Increase the number of training instances to distribute the workload.
Why wrong: Increasing instances does not fix a code or environment issue.
- E
Run the container locally using SageMaker Local Mode to simulate the training environment.
Local mode allows debugging without incurring cloud costs.
Quick Answer
The answer is to check CloudWatch logs, run the container locally using SageMaker Local Mode, and use SageMaker Debugger. These three steps are correct because a non-zero exit code indicates the container process failed, and CloudWatch logs provide the runtime error output, Local Mode replicates the SageMaker environment on your machine for detailed debugging, and SageMaker Debugger captures tensors and system metrics to pinpoint training issues. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this question tests your understanding of SageMaker’s debugging workflow and common pitfalls like assuming retries or scaling will fix code-level failures. A common trap is choosing “increase instance count” or “retry the job,” which only mask the underlying bug. Remember the mnemonic “CLD” — CloudWatch, Local, Debugger — to recall the three diagnostic steps for a non-zero exit code.
MLS-C01 Practice Question: Machine Learning Implementation and Operations
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 data scientist is using Amazon SageMaker to train a model using a custom Docker container. The training job fails with an error message indicating that the container exited with a non-zero code. Which THREE steps should the data scientist take to diagnose the issue? (Choose THREE.)
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
Use the SageMaker Debugger to capture system metrics and output tensors for analysis.
Option A, B, and D are correct because checking CloudWatch logs, testing locally with SageMaker Local Mode, and using the SageMaker Debugger can help identify the error. Option C is wrong because increasing instance count does not fix the error. Option E is wrong because Retry may mask the issue.
Key principle: Count usable hosts — not total addresses — and remember that the network and broadcast addresses are not available to hosts in standard IPv4 subnets.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Retry the training job with the same configuration; the error might be transient.
Why it's wrong here
Retrying without diagnosis will likely fail again.
- ✓
Use the SageMaker Debugger to capture system metrics and output tensors for analysis.
Why this is correct
Debugger can capture detailed metrics that help identify why the container exited.
Related concept
CIDR notation defines the prefix length.
- ✓
Check the CloudWatch Logs for the training job to see the container's stdout and stderr.
Why this is correct
CloudWatch logs capture the container's output, which often contains error details.
Related concept
CIDR notation defines the prefix length.
- ✗
Increase the number of training instances to distribute the workload.
Why it's wrong here
Increasing instances does not fix a code or environment issue.
- ✓
Run the container locally using SageMaker Local Mode to simulate the training environment.
Why this is correct
Local mode allows debugging without incurring cloud costs.
Related concept
CIDR notation defines the prefix length.
Common exam traps
Common exam trap: usable hosts are not the same as total addresses
Subnetting questions often tempt you into counting all addresses. In normal IPv4 subnets, the network and broadcast addresses are not usable host addresses.
Detailed technical explanation
How to think about this question
Subnetting questions test whether you can identify the network, broadcast address, usable range, mask and correct subnet. Slow down enough to calculate the block size correctly.
KKey Concepts to Remember
- CIDR notation defines the prefix length.
- Block size helps identify subnet boundaries.
- Network and broadcast addresses are not usable hosts in normal IPv4 subnets.
- The required host count determines the smallest suitable subnet.
TExam Day Tips
- Write the block size before choosing the subnet.
- Check whether the question asks for hosts, subnets or a specific address range.
- Do not confuse /24, /25, /26 and /27 host counts.
Key takeaway
Count usable hosts — not total addresses — and remember that the network and broadcast addresses are not available to hosts in standard IPv4 subnets.
Real-world example
How this comes up in practice
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. Count usable hosts — not total addresses — and remember that the network and broadcast addresses are not available to hosts in standard IPv4 subnets. 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
Got this wrong? Here's your next step.
Review block sizes, usable host formulas (2^n − 2), and how to find network and broadcast addresses for /24 through /30. Then practise related MLS-C01 subnetting questions on CIDR, address ranges, and subnet selection.
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FAQ
Questions learners often ask
What does this MLS-C01 question test?
Machine Learning Implementation and Operations — This question tests Machine Learning Implementation and Operations — CIDR notation defines the prefix length..
What is the correct answer to this question?
The correct answer is: Use the SageMaker Debugger to capture system metrics and output tensors for analysis. — Option A, B, and D are correct because checking CloudWatch logs, testing locally with SageMaker Local Mode, and using the SageMaker Debugger can help identify the error. Option C is wrong because increasing instance count does not fix the error. Option E is wrong because Retry may mask the issue.
What should I do if I get this MLS-C01 question wrong?
Review block sizes, usable host formulas (2^n − 2), and how to find network and broadcast addresses for /24 through /30. Then practise related MLS-C01 subnetting questions on CIDR, address ranges, and subnet selection.
What is the key concept behind this question?
CIDR notation defines the prefix length.
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Last reviewed: Jun 20, 2026
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