Question 891 of 1,755
Machine Learning Implementation and OperationseasyMultiple ChoiceObjective-mapped

Quick Answer

The answer is the ml.p3.2xlarge instance type. This is correct because the P3 family is specifically designed for GPU-accelerated compute, utilizing NVIDIA Tesla V100 GPUs to handle the parallel processing demands of deep learning inference. In contrast, CPU-only instances like the ml.m5, ml.c5, or ml.t2 families lack the dedicated hardware required for efficient GPU inference, making them unsuitable for models that rely on GPU computation. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this question tests your ability to match workload requirements to the correct SageMaker instance type, often appearing as a straightforward discrimination between GPU and CPU families. A common trap is confusing the general-purpose or compute-optimized instances (like m5 or c5) with GPU-capable ones, so remember that any instance starting with "p" or "g" indicates GPU support. Memory tip: "P for Parallel Processing" helps you recall that P3 instances are your go-to for GPU inference.

MLS-C01 Practice Question: Machine Learning Implementation and Operations

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 machine learning engineer is deploying a model to an Amazon SageMaker endpoint. The model requires GPU for inference. Which instance type should be selected?

Question 1easymultiple choice
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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

ml.p3.2xlarge

Option D is correct because P3 instances (e.g., ml.p3.2xlarge) provide GPU capabilities. Options A, B, and C are CPU-only instances.

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.

  • ml.p3.2xlarge

    Why this is correct

    GPU instance suitable for inference.

    Related concept

    Read the scenario before looking for a memorised answer.

  • ml.m5.large

    Why it's wrong here

    General purpose CPU instance.

  • ml.c5.xlarge

    Why it's wrong here

    Compute optimized CPU instance.

  • ml.r5.large

    Why it's wrong here

    Memory optimized CPU instance.

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.

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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: ml.p3.2xlarge — Option D is correct because P3 instances (e.g., ml.p3.2xlarge) provide GPU capabilities. Options A, B, and C are CPU-only instances.

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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Last reviewed: Jun 20, 2026

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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.