- A
Switch to a multi-model endpoint to share instances across models
Why wrong: Multi-model endpoints still have fixed instances; cost savings are limited.
- B
Reduce the number of instances to one
Why wrong: Reducing instances may cause high latency during traffic spikes.
- C
Migrate to a SageMaker Serverless Inference endpoint
Serverless endpoints scale to zero when idle, reducing cost.
- D
Implement an asynchronous inference endpoint
Why wrong: Asynchronous inference is for batch processing, not real-time.
Question 484 of 1,000
MLA-C01 Practice Question: ML Solution Monitoring, Maintenance and Security
This MLA-C01 practice question tests your understanding of ml solution monitoring, maintenance and security. 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 team deploys a machine learning model using a SageMaker endpoint with an ML.T4 instance. After a week, they notice that the endpoint's CPU utilization is consistently below 10% and latency is low. However, the endpoint is incurring high costs. Which action should the team take to reduce costs while maintaining the ability to serve traffic?
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
✓
Migrate to a SageMaker Serverless Inference endpoint
Option C is correct because the endpoint's CPU utilization is consistently below 10% with low latency, indicating that traffic is sparse and the instance is severely underutilized. SageMaker Serverless Inference automatically scales compute resources based on request volume and charges only for the compute time consumed per inference, eliminating idle costs. This makes it the most cost-effective choice for low-utilization workloads while still maintaining the ability to serve traffic on demand.
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 to a multi-model endpoint to share instances across models
Why it's wrong here
Multi-model endpoints still have fixed instances; cost savings are limited.
- ✗
Reduce the number of instances to one
Why it's wrong here
Reducing instances may cause high latency during traffic spikes.
- ✓
Migrate to a SageMaker Serverless Inference endpoint
Why this is correct
Serverless endpoints scale to zero when idle, reducing cost.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Implement an asynchronous inference endpoint
Why it's wrong here
Asynchronous inference is for batch processing, not real-time.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often assume reducing instance count or switching endpoint types (multi-model, async) will lower costs, but they overlook that provisioned instances always incur hourly charges, whereas serverless charges only for actual compute usage, making it the optimal choice for consistently low-utilization endpoints.
Detailed technical explanation
How to think about this question
SageMaker Serverless Inference uses a 'provisioned concurrency' model where you set a maximum concurrency limit, and AWS scales the underlying compute from zero to that limit, charging per millisecond of inference time plus data transfer. Under the hood, it leverages AWS Lambda-like infrastructure with a cold start penalty (typically 1–5 seconds) for the first request after idle, which is acceptable for low-latency, low-traffic workloads. In real-world scenarios, a team running a chatbot or periodic batch prediction API would see costs drop from hundreds of dollars per month to a few dollars with serverless.
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.
Quick reference
Cloud Service Model Comparison
| Model | You Manage | Provider Manages | Examples |
|---|---|---|---|
| IaaS | OS, runtime, apps, data | Hardware, hypervisor, networking | EC2, Azure VMs, GCP Compute Engine |
| PaaS | Apps and data | OS, runtime, middleware, hardware | Elastic Beanstalk, Azure App Service |
| SaaS | Data and settings only | Everything else | Microsoft 365, Salesforce, Workday |
| FaaS / Serverless | Function code only | Infra, scaling, runtime | Lambda, Azure Functions, Cloud Run |
| CaaS | Containers and apps | Kubernetes, OS, hardware | EKS, AKS, GKE |
What to study next
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FAQ
Questions learners often ask
What does this MLA-C01 question test?
ML Solution Monitoring, Maintenance and Security — This question tests ML Solution Monitoring, Maintenance and Security — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Migrate to a SageMaker Serverless Inference endpoint — Option C is correct because the endpoint's CPU utilization is consistently below 10% with low latency, indicating that traffic is sparse and the instance is severely underutilized. SageMaker Serverless Inference automatically scales compute resources based on request volume and charges only for the compute time consumed per inference, eliminating idle costs. This makes it the most cost-effective choice for low-utilization workloads while still maintaining the ability to serve traffic on demand.
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.
About these practice questions
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Last reviewed: Jul 4, 2026
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