The answer is SageMaker real-time endpoints, batch transform, and Elastic Inference. These three configurations support GPU for inference because real-time endpoints and batch transform both allow you to select GPU-backed instance types like the P3 or G4 families, which provide the parallel processing power needed for deep learning models. Elastic Inference, meanwhile, attaches a fractional GPU accelerator to a CPU instance, offering a cost-effective way to speed up inference without provisioning a full GPU instance. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this question tests your understanding of SageMaker inference deployment options and their hardware constraints; a common trap is assuming Serverless inference supports GPU, which it does not, or confusing SageMaker Studio—an IDE—with an inference endpoint. Remember the mnemonic “REB” for Real-time, Elastic, Batch—the three GPU-capable paths, while Serverless and Studio are the distractors.
MLS-C01 Modeling Practice Question
This MLS-C01 practice question tests your understanding of modeling. 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.
A company is deploying a machine learning model using Amazon SageMaker. The model requires GPUs for inference. Which THREE configurations can the company use to meet this requirement? (Choose THREE.)
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Real-time endpoints with ml.p3 instance types
Real-time endpoints support GPU instances. Batch transform also supports GPU. Elastic Inference (option C) provides GPU acceleration without a full GPU instance. Option B (Serverless) does not support GPU. Option D (SageMaker Studio) is an IDE, not for inference.
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.
✗
SageMaker Serverless Inference
Why it's wrong here
Serverless Inference does not support GPU.
✓
Real-time endpoints with ml.p3 instance types
Why this is correct
Real-time endpoints support GPU instances like ml.p3.
Related concept
Read the scenario before looking for a memorised answer.
✓
SageMaker Batch Transform with ml.p3 instances
Why this is correct
Batch Transform can use GPU instances for offline inference.
Related concept
Read the scenario before looking for a memorised answer.
✗
SageMaker Studio
Why it's wrong here
SageMaker Studio is a development environment, not for inference.
✓
SageMaker Elastic Inference (EI)
Why this is correct
Elastic Inference provides GPU acceleration for inference.
Related concept
Read the scenario before looking for a memorised answer.
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 cloud solutions architect for a retail company is evaluating services for a new workload. The correct answer here reflects best practice for the specific scenario described — not a general cloud recommendation. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Cloud exam questions reward reading the constraint carefully: the same technology can be right or wrong depending on the use case.
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.
Modeling — This question tests Modeling — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Real-time endpoints with ml.p3 instance types — Real-time endpoints support GPU instances. Batch transform also supports GPU. Elastic Inference (option C) provides GPU acceleration without a full GPU instance. Option B (Serverless) does not support GPU. Option D (SageMaker Studio) is an IDE, not for inference.
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.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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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.
Question Discussion
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