The answer is to enable CloudWatch metrics for the training job and view GPU utilization in the CloudWatch console. This approach is most effective because SageMaker automatically publishes built-in CloudWatch metrics for GPU utilization, memory utilization, and other hardware-level performance counters when you launch a training job on a GPU instance, eliminating the need for any custom instrumentation. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this question tests your understanding of SageMaker’s native observability features versus over-engineering solutions; a common trap is choosing a custom script or SageMaker Debugger, which is designed for profiling and debugging bottlenecks, not real-time monitoring. Remember that for real-time monitoring of GPU utilization during SageMaker training, CloudWatch is the default and most direct tool—think “CloudWatch watches the GPU, no custom hocus-pocus.”
MLS-C01 Practice Question: Machine Learning Implementation and Operations
This MLS-C01 practice question tests your understanding of machine learning implementation and operations. 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.
A company is using Amazon SageMaker to train a large natural language processing model. The training job uses a GPU instance and is expected to take several hours. The data scientist wants to monitor GPU utilization in real-time. Which approach is MOST effective?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Enable CloudWatch metrics for the training job and view GPU utilization in the CloudWatch console
Option A is correct because SageMaker publishes CloudWatch metrics for GPU utilization. Option B uses a custom solution when a built-in one exists. Option C is for debugging, not real-time monitoring. Option D is for managed spot training, not monitoring.
Key principle: NAT direction and interface roles matter as much as the IP address mapping. Inside/outside designation controls which traffic is translated.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
✗
Use SageMaker Managed Spot Training to reduce cost and monitor utilization via spot instance status
Why it's wrong here
Spot status does not provide GPU utilization.
✗
Modify the training script to periodically log GPU utilization to a file in S3
Why it's wrong here
This is more complex and not real-time.
✗
Use SageMaker Debugger to capture GPU utilization tensors
Why it's wrong here
Debugger is for tensors and gradients, not real-time metrics.
✓
Enable CloudWatch metrics for the training job and view GPU utilization in the CloudWatch console
Why this is correct
SageMaker automatically publishes GPU metrics to CloudWatch.
Related concept
Static NAT maps one inside address to one outside address.
Common exam traps
Common exam trap: NAT rules depend on direction and matching traffic
NAT is not only about the public address. The inside/outside interface roles and the ACL or rule that matches traffic are just as important.
Detailed technical explanation
How to think about this question
NAT questions usually test address translation, overload/PAT behaviour, static mappings and whether the right traffic is being translated. Read the interface direction and address terms carefully.
KKey Concepts to Remember
Static NAT maps one inside address to one outside address.
PAT allows many inside hosts to share one public address using ports.
Inside local and inside global describe the private and translated addresses.
NAT ACLs identify traffic for translation, not always security filtering.
TExam Day Tips
→Identify inside and outside interfaces first.
→Check whether the scenario needs static NAT, dynamic NAT or PAT.
→Do not confuse NAT matching ACLs with normal packet-filtering intent.
Key takeaway
NAT direction and interface roles matter as much as the IP address mapping. Inside/outside designation controls which traffic is translated.
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.
Review the four NAT address types (inside local, inside global, outside local, outside global), PAT port overload, and static vs dynamic NAT use cases. Then practise related MLS-C01 NAT questions on configuration and troubleshooting.
Machine Learning Implementation and Operations — This question tests Machine Learning Implementation and Operations — Static NAT maps one inside address to one outside address..
What is the correct answer to this question?
The correct answer is: Enable CloudWatch metrics for the training job and view GPU utilization in the CloudWatch console — Option A is correct because SageMaker publishes CloudWatch metrics for GPU utilization. Option B uses a custom solution when a built-in one exists. Option C is for debugging, not real-time monitoring. Option D is for managed spot training, not monitoring.
What should I do if I get this MLS-C01 question wrong?
Review the four NAT address types (inside local, inside global, outside local, outside global), PAT port overload, and static vs dynamic NAT use cases. Then practise related MLS-C01 NAT questions on configuration and troubleshooting.
What is the key concept behind this question?
Static NAT maps one inside address to one outside address.
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Question Discussion
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