The answer is GPU memory and cost per inference. When selecting a GPU instance for SageMaker real-time inference, the GPU memory must be large enough to hold the entire model along with a batch of input images, as insufficient memory causes out-of-memory errors or forces smaller batch sizes that increase latency. Equally critical is the cost per inference, which directly impacts operational efficiency and long-term deployment viability. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this question tests your understanding that GPU inference optimization prioritizes memory capacity and cost over CPU-centric metrics like vCPUs or unrelated factors like S3 location. A common trap is focusing on training time or vCPU count, but for inference, the GPU’s memory bandwidth and total VRAM are what matter. Remember the mnemonic “Mem-Cost for Inference”—always check memory first, then cost per prediction.
MLS-C01 Practice Question: Machine Learning Implementation and Operations
This MLS-C01 practice question tests your understanding of machine learning implementation and operations. Match the stated requirement to the specific cloud service, access model, or configuration option — many options are valid in isolation but not for this scenario. 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 on Amazon SageMaker. The model requires inference on images, and the data scientist wants to use a GPU instance for low latency. However, the data scientist is unsure about the instance type to choose for the endpoint. Which TWO factors should the data scientist consider when selecting the instance type? (Choose TWO.)
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
The cost per inference for the instance type
Options B and D are correct. B: GPU memory must be sufficient to hold the model and a batch of images. D: Cost per inference is important for operational efficiency. Option A (number of vCPUs) is less relevant for GPU inference. Option C (S3 bucket location) does not affect instance choice. Option E (training time) is not relevant 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.
✗
The time taken to train the model
Why it's wrong here
Training time is irrelevant for inference instance selection.
✗
The number of vCPUs on the instance
Why it's wrong here
vCPUs are less important for GPU inference.
✓
The cost per inference for the instance type
Why this is correct
Cost is a key consideration.
Related concept
Read the scenario before looking for a memorised answer.
✗
The AWS Region of the S3 bucket storing the model
Why it's wrong here
Region does not affect instance type.
✓
The GPU memory available on the instance
Why this is correct
GPU memory must fit model and input.
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 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
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.
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: The cost per inference for the instance type — Options B and D are correct. B: GPU memory must be sufficient to hold the model and a batch of images. D: Cost per inference is important for operational efficiency. Option A (number of vCPUs) is less relevant for GPU inference. Option C (S3 bucket location) does not affect instance choice. Option E (training time) is not relevant 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.
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 →
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 data scientist is deploying a model using Amazon SageMaker for real-time inference. The model is memory-intensive and requires a GPU. Which instance type should be selected for the endpoint?
easy
A.i3.2xlarge
B.c5.2xlarge
C.r5.2xlarge
✓ D.p3.2xlarge
Why D: The p3.2xlarge instance is correct because it provides a GPU (NVIDIA Tesla V100) with high memory bandwidth, which is essential for memory-intensive deep learning models requiring GPU acceleration for real-time inference. SageMaker endpoints for GPU-based models must use instance types from the P or G families, as CPU-only instances like i3, c5, or r5 lack the parallel processing capabilities needed for efficient GPU inference.
Last reviewed: Jun 20, 2026
Question Discussion
Share a tip, memory trick, or ask about the reasoning behind this question. Do not post real exam questions, leaked content, braindumps, or copyrighted exam material. Comments are moderated and may be removed without notice.
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
Share a tip, memory trick, or ask about the reasoning behind this question. Do not post real exam questions, leaked content, braindumps, or copyrighted exam material. Comments are moderated and may be removed without notice.
Sign in to join the discussion.