- A
Use a larger instance type
Why wrong: Increases cost, may over-provision.
- B
Deploy to a single instance
Why wrong: May cause latency spikes and is not cost-optimized.
- C
Switch to batch transform
Why wrong: Not suitable for real-time inference.
- D
Use SageMaker Serverless Inference
Pay per inference, scales automatically, cost-effective.
- E
Enable Auto Scaling on the endpoint
Scales based on demand, reduces cost during off-peak.
Question 125 of 1,755
Reducing SageMaker Inference Costs Without Sacrificing Latency: Serverless and Auto Scaling
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 data scientist is deploying a model to a SageMaker endpoint and needs to optimize for cost while maintaining low latency. Which TWO actions should the data scientist take?
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 SageMaker Serverless Inference
SageMaker Serverless Inference (Option D) automatically scales compute resources based on request volume, charging only for the compute time used during inference. This eliminates the cost of idle provisioned instances, making it ideal for optimizing cost while maintaining low latency for variable or intermittent traffic patterns.
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.
- ✗
Use a larger instance type
Why it's wrong here
Increases cost, may over-provision.
- ✗
Deploy to a single instance
Why it's wrong here
May cause latency spikes and is not cost-optimized.
- ✗
Switch to batch transform
Why it's wrong here
Not suitable for real-time inference.
- ✓
Use SageMaker Serverless Inference
Why this is correct
Pay per inference, scales automatically, cost-effective.
Related concept
Read the scenario before looking for a memorised answer.
- ✓
Enable Auto Scaling on the endpoint
Why this is correct
Scales based on demand, reduces cost during off-peak.
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 assume 'larger instances' or 'single instance' are cost-saving measures, but the exam tests understanding that cost optimization for variable traffic requires dynamic scaling (Auto Scaling) or fully serverless compute, not static instance choices.
Detailed technical explanation
How to think about this question
SageMaker Serverless Inference uses a 'warm pool' model where the inference container is kept ready but not billed until a request arrives; it scales from zero to a configured maximum concurrency. Auto Scaling (Option E) on a real-time endpoint dynamically adjusts the number of instances based on a target metric (e.g., CPU utilization or request latency), reducing costs during low traffic while ensuring enough capacity during spikes. Both approaches avoid over-provisioning, but Serverless is fully managed with no instance management, while Auto Scaling requires setting scaling policies and cooldown periods.
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 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: Use SageMaker Serverless Inference — SageMaker Serverless Inference (Option D) automatically scales compute resources based on request volume, charging only for the compute time used during inference. This eliminates the cost of idle provisioned instances, making it ideal for optimizing cost while maintaining low latency for variable or intermittent traffic patterns.
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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