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 wants to deploy a machine learning model that requires GPU acceleration for inference. The model is small and can fit on a single GPU. Which SageMaker endpoint configuration is MOST cost-effective?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Use a ml.p3.2xlarge instance with 1 GPU and enable automatic scaling.
Option D is the most cost-effective because it uses a single-GPU ml.p3.2xlarge instance, which matches the requirement that the model fits on one GPU, and enables automatic scaling to handle variable traffic without over-provisioning. This avoids paying for unused GPU capacity while still providing the necessary GPU acceleration 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.
✗
Use a ml.p3.16xlarge instance with 8 GPUs.
Why it's wrong here
This instance is over-provisioned and costly for a small model.
✗
Use a SageMaker Serverless Inference endpoint.
Why it's wrong here
Serverless Inference does not support GPU instances.
✗
Use a Multi-Model Endpoint on a ml.g4dn.xlarge instance.
Why it's wrong here
Multi-Model Endpoints are for hosting multiple models; a single model may not need this complexity.
✓
Use a ml.p3.2xlarge instance with 1 GPU and enable automatic scaling.
Why this is correct
A single GPU instance with scaling provides cost-effective real-time inference.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often choose a larger GPU instance (like A) thinking it provides better performance, or select Serverless Inference (B) assuming it supports all instance types, but the exam tests the specific constraint that GPU acceleration is required and that Serverless Inference is CPU-only.
Detailed technical explanation
How to think about this question
Under the hood, SageMaker automatic scaling uses target tracking policies based on metrics like InvocationsPerInstance to dynamically adjust the number of instances, ensuring you only pay for the compute you use. The ml.p3.2xlarge instance provides a single NVIDIA V100 GPU with 16 GB of GPU memory, which is sufficient for many small to medium-sized models, and its on-demand pricing is significantly lower than larger instances. In a real-world scenario, a company might deploy a fine-tuned BERT-base model for real-time inference, where a single GPU handles latency requirements without needing multi-GPU parallelism.
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.
TExam Day Tips
→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 exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
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: Use a ml.p3.2xlarge instance with 1 GPU and enable automatic scaling. — Option D is the most cost-effective because it uses a single-GPU ml.p3.2xlarge instance, which matches the requirement that the model fits on one GPU, and enables automatic scaling to handle variable traffic without over-provisioning. This avoids paying for unused GPU capacity while still providing the necessary GPU acceleration for inference.
What should I do if I get this MLS-C01 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
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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