Quick Answer
The correct choice is to use a real-time endpoint with a GPU instance and enable multi-model endpoints. This deployment strategy minimizes both latency and cost because a multi-model endpoint allows you to host multiple deep learning models on a single GPU instance, sharing the underlying hardware while serving each model in real time. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this question tests your understanding of SageMaker inference options and their trade-offs; a common trap is confusing batch or asynchronous inference with real-time requirements, or assuming Serverless Inference supports GPUs (it does not). Remember that for GPU-accelerated, low-latency serving, you need a real-time endpoint, and multi-model endpoints are the cost-saving mechanism that lets you pack several models onto one GPU instance. A useful memory tip: “Real-time needs GPU, multi-model saves you the money.”
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 deploying a real-time inference endpoint using Amazon SageMaker. The model is a large deep learning model that requires GPU inference. The company wants to minimize latency and cost. Which instance type and deployment strategy should be used?
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.
Use a serverless inference endpoint with a GPU instance.
Why wrong: Serverless does not support GPUs.
Use a real-time endpoint with a GPU instance and enable multi-model endpoints.
Multi-model endpoints reduce cost by sharing GPU across models.
Use a batch transform job with a GPU instance.
Why wrong: Batch transform is not real-time.
Use an asynchronous inference endpoint with a GPU instance.
Why wrong: Asynchronous is not real-time.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
Use a real-time endpoint with a GPU instance and enable multi-model endpoints.
Option C is correct because SageMaker real-time endpoints with multi-model endpoints allow hosting multiple models on a single GPU instance, reducing cost while maintaining low latency. Option A is wrong because batch transform is not real-time. Option B is wrong because Serverless Inference does not support GPUs. Option D is wrong because asynchronous inference is not real-time.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
For each option: why learners choose it and why it is or isn't the right answer here.
Use a serverless inference endpoint with a GPU instance.
Why it's wrong here
Serverless does not support GPUs.
Use a real-time endpoint with a GPU instance and enable multi-model endpoints.
Why this is correct
Multi-model endpoints reduce cost by sharing GPU across models.
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 with a GPU instance.
Why it's wrong here
Batch transform is not real-time.
Use an asynchronous inference endpoint with a GPU instance.
Why it's wrong here
Asynchronous is not real-time.
Common exam traps
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
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.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
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
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
Machine Learning Implementation and Operations — This question tests Machine Learning Implementation and Operations — Read the scenario before looking for a memorised answer..
The correct answer is: Use a real-time endpoint with a GPU instance and enable multi-model endpoints. — Option C is correct because SageMaker real-time endpoints with multi-model endpoints allow hosting multiple models on a single GPU instance, reducing cost while maintaining low latency. Option A is wrong because batch transform is not real-time. Option B is wrong because Serverless Inference does not support GPUs. Option D is wrong because asynchronous inference is not real-time.
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.
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.
Read the scenario before looking for a memorised answer.
About these practice questions
Courseiva creates original exam-style practice questions with explanations and wrong-answer analysis. It does not publish real exam questions, exam dumps, or protected exam content. Learn why practice questions differ from exam dumps →
Same concept, more angles
These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.
Variation 1. A company is deploying a real-time inference endpoint using Amazon SageMaker. The model is a large deep learning model that requires low latency. The team is concerned about cost. Which SageMaker hosting option should the team use?
mediumWhy D: Option C is correct because multi-model endpoints share resources and reduce cost. Option A is wrong because it's for testing. Option B is wrong because serverless can have cold starts. Option D is wrong because batch transform is not real-time.
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Last reviewed: Jun 20, 2026
This MLS-C01 practice question is part of Courseiva's free Amazon Web Services certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the MLS-C01 exam.
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