- A
Use a GPU instance (ml.p3.2xlarge) with SageMaker Elastic Inference.
Elastic Inference provides GPU acceleration at lower cost than a full GPU instance.
- B
Use a batch transform job on GPU instances.
Why wrong: Batch transform is for offline predictions, not real-time.
- C
Use a serverless inference endpoint with a CPU instance.
Why wrong: Serverless does not support GPU and may have cold start latency.
- D
Use a multi-model endpoint on a GPU instance.
Why wrong: Multi-model endpoints are for cost savings when hosting many models, not specifically for latency.
Question 752 of 1,755
MLS-C01 Practice Question: Machine Learning Implementation and Operations
This MLS-C01 practice question tests your understanding of machine learning implementation and operations. This is a configuration task: choose the command set that satisfies every stated requirement. Small differences — like 'secret' vs 'password' or 'transport input ssh' vs 'all' — change whether the answer is correct. 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.
An ML team is deploying a model to a SageMaker endpoint for real-time inference. The model is large (2 GB) and requires GPU for low-latency inference. The team wants to minimize cost while maintaining a response time of under 200 ms. Which instance configuration and SageMaker feature would be best?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue:
"minimum / minimize"Why it matters: Asks for the least resource use — fewest addresses, smallest subnet, lowest overhead. Eliminate over-provisioned options even if they would technically work.
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 a GPU instance (ml.p3.2xlarge) with SageMaker Elastic Inference.
Option A is the best choice. By using a GPU instance (ml.p3.2xlarge) combined with SageMaker Elastic Inference, the team can attach a fractional GPU accelerator to the instance, providing the necessary GPU compute for low-latency inference while minimizing cost compared to using a full GPU instance alone. Option B (batch transform) is designed for offline inference on large datasets, not real-time. Option C (serverless inference) typically uses CPU instances and may incur cold starts, which can exceed the 200 ms response time requirement. Option D (multi-model endpoint) is optimized for hosting multiple models on a single instance, but does not directly address GPU acceleration or cost minimization for a single large model.
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 GPU instance (ml.p3.2xlarge) with SageMaker Elastic Inference.
Why this is correct
Elastic Inference provides GPU acceleration at lower cost than a full GPU instance.
Clue confirmation
The clue word "minimum / minimize" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Use a batch transform job on GPU instances.
Why it's wrong here
Batch transform is for offline predictions, not real-time.
- ✗
Use a serverless inference endpoint with a CPU instance.
Why it's wrong here
Serverless does not support GPU and may have cold start latency.
- ✗
Use a multi-model endpoint on a GPU instance.
Why it's wrong here
Multi-model endpoints are for cost savings when hosting many models, not specifically for latency.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Many certification questions include familiar terms but test a specific constraint. Read the exact wording before choosing an answer that is generally true but wrong for this case.
Detailed technical explanation
How to think about this question
This question should be treated as a scenario, not a definition check. Identify the problem, the constraint and the best action. Then compare each option against those facts.
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.
- Use explanations to understand the rule behind the answer.
TExam Day Tips
- Underline the problem statement mentally.
- 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
Got this wrong? Here's your next step.
Identify which MLS-C01 exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
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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 a GPU instance (ml.p3.2xlarge) with SageMaker Elastic Inference. — Option A is the best choice. By using a GPU instance (ml.p3.2xlarge) combined with SageMaker Elastic Inference, the team can attach a fractional GPU accelerator to the instance, providing the necessary GPU compute for low-latency inference while minimizing cost compared to using a full GPU instance alone. Option B (batch transform) is designed for offline inference on large datasets, not real-time. Option C (serverless inference) typically uses CPU instances and may incur cold starts, which can exceed the 200 ms response time requirement. Option D (multi-model endpoint) is optimized for hosting multiple models on a single instance, but does not directly address GPU acceleration or cost minimization for a single large model.
What should I do if I get this MLS-C01 question wrong?
Identify which MLS-C01 exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
Are there clue words in this question I should notice?
Yes — watch for: "minimum / minimize". Asks for the least resource use — fewest addresses, smallest subnet, lowest overhead. Eliminate over-provisioned options even if they would technically work.
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: Jun 20, 2026
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