The answer is to deploy the PyTorch model on an ml.p3.2xlarge instance with a SageMaker endpoint. This is correct because real-time inference requires a persistent endpoint that can handle synchronous requests with low latency, and GPU instances like the ml.p3.2xlarge provide the parallel processing power needed for deep learning models. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this question tests your understanding of SageMaker inference deployment options, specifically the distinction between real-time endpoints and batch transform jobs. A common trap is confusing batch transform with real-time inference—batch is asynchronous and does not provide a live endpoint. Remember the key rule: if the requirement is low-latency, real-time predictions, you must use a SageMaker endpoint with a GPU instance, not a CPU-only instance or a batch job. Memory tip: “Real-time needs a live endpoint; batch is for offline.”
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 machine learning engineer is deploying a PyTorch model on SageMaker for real-time inference. The model requires GPU for low latency. Which instance type and configuration should the engineer choose?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Deploy to an ml.p3.2xlarge instance with a SageMaker endpoint.
SageMaker real-time endpoints support GPU instances like ml.p3.2xlarge. Option A (ml.m5.large) is CPU only. Option B (ml.c5.4xlarge) is CPU. Option D (ml.p3.2xlarge with batch transform) is inference but batch is not real-time; endpoint is needed.
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.
✗
Deploy to an ml.c5.4xlarge instance with SageMaker batch transform.
Why it's wrong here
Batch transform is for offline inference, not real-time.
✗
Deploy to an ml.m5.large instance with a SageMaker model endpoint.
Why it's wrong here
ml.m5 is CPU only, no GPU.
✓
Deploy to an ml.p3.2xlarge instance with a SageMaker endpoint.
Read the scenario before looking for a memorised answer.
✗
Deploy to an ml.p3.2xlarge instance with SageMaker batch transform.
Why it's wrong here
Batch transform is not real-time; endpoint is required.
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: Deploy to an ml.p3.2xlarge instance with a SageMaker endpoint. — SageMaker real-time endpoints support GPU instances like ml.p3.2xlarge. Option A (ml.m5.large) is CPU only. Option B (ml.c5.4xlarge) is CPU. Option D (ml.p3.2xlarge with batch transform) is inference but batch is not real-time; endpoint is needed.
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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