- A
Configure SageMaker automatic scaling based on CPU utilization
Auto scaling dynamically adjusts instance count to handle load cost-effectively.
- B
Use CloudWatch alarms to notify the team, who manually launch additional endpoints
Why wrong: Manual intervention is slow and not cost-effective.
- C
Migrate the model to AWS Lambda with provisioned concurrency
Why wrong: Lambda has memory and timeout limits unsuitable for large ensemble models.
- D
Replace the current instance type with a larger one
Why wrong: Vertical scaling is less cost-effective than horizontal scaling for variable loads.
Question 815 of 1,755
Automatic Scaling for SageMaker Endpoints: Cost-Effective CPU-Based Solutions
This MLS-C01 practice question tests your understanding of machine learning implementation and operations. Examine the command output carefully: the correct answer depends on what the output actually shows, not on general recall alone. 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 deploys a SageMaker model for inference. After a few days, response times increase significantly. CloudWatch metrics show high CPU utilization and memory usage. The model is a large ensemble. What is the most cost-effective solution?
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
✓
Configure SageMaker automatic scaling based on CPU utilization
SageMaker automatic scaling based on CPU utilization is the most cost-effective solution because it dynamically adjusts the number of inference instances in response to real-time demand, adding capacity only when CPU usage is high and removing it when demand drops. This avoids over-provisioning while maintaining performance for the large ensemble model, which is compute-intensive. Other options either introduce manual overhead, are unsuitable for large models, or incur unnecessary cost by permanently using larger instances.
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.
- ✓
Configure SageMaker automatic scaling based on CPU utilization
Why this is correct
Auto scaling dynamically adjusts instance count to handle load cost-effectively.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Use CloudWatch alarms to notify the team, who manually launch additional endpoints
Why it's wrong here
Manual intervention is slow and not cost-effective.
- ✗
Migrate the model to AWS Lambda with provisioned concurrency
Why it's wrong here
Lambda has memory and timeout limits unsuitable for large ensemble models.
- ✗
Replace the current instance type with a larger one
Why it's wrong here
Vertical scaling is less cost-effective than horizontal scaling for variable loads.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often choose manual scaling (Option B) or vertical scaling (Option D) because they seem simpler, but the exam tests the understanding that automatic horizontal scaling is the most cost-effective and operationally efficient approach for handling variable inference workloads in SageMaker.
Detailed technical explanation
How to think about this question
SageMaker automatic scaling uses the AWS Application Auto Scaling service, which relies on CloudWatch alarms to trigger scaling policies based on metrics like CPU utilization or memory usage. For large ensemble models, it is critical to set appropriate cooldown periods (e.g., 300 seconds) to avoid thrashing, and to define both scale-out and scale-in policies to ensure cost efficiency. In real-world scenarios, a model that uses multiple algorithms (e.g., XGBoost, Random Forest, and a neural network) can cause CPU spikes during inference, making CPU-based scaling more responsive than request-based metrics like invocations per instance.
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
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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 — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Configure SageMaker automatic scaling based on CPU utilization — SageMaker automatic scaling based on CPU utilization is the most cost-effective solution because it dynamically adjusts the number of inference instances in response to real-time demand, adding capacity only when CPU usage is high and removing it when demand drops. This avoids over-provisioning while maintaining performance for the large ensemble model, which is compute-intensive. Other options either introduce manual overhead, are unsuitable for large models, or incur unnecessary cost by permanently using larger instances.
What should I do if I get this MLS-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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Last reviewed: Jul 4, 2026
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