The correct choice is a production variant endpoint with auto-scaling based on CPU utilization. This configuration directly addresses the challenge of auto scaling SageMaker endpoint cost spikes by dynamically adjusting instance count in response to real-time load, scaling up during business hours to maintain availability and scaling down during off-peak periods to minimize cost. Since scikit-learn models are CPU-bound, CPU utilization is the most relevant metric for triggering scaling actions, ensuring the endpoint handles traffic spikes efficiently without over-provisioning. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this scenario tests your understanding of SageMaker production variants and auto-scaling policies, often appearing as a trap where candidates mistakenly choose GPU-based scaling or manual instance management. A key memory tip: for CPU-bound models like scikit-learn, think “CPU for compute” to avoid selecting memory or GPU metrics, and remember that production variants allow separate scaling policies per model version.
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 team is deploying a SageMaker endpoint for a model that was trained with scikit-learn. The endpoint receives spikes in traffic during business hours. The team wants to minimize cost while ensuring availability during spikes. Which endpoint configuration is MOST appropriate?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue: "minimum / minimize"
Why it matters: Asks for the least resource use — fewest addresses, smallest subnet, lowest overhead. Eliminate over-provisioned options even if they would technically work.
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Use a production variant endpoint with auto-scaling based on CPU utilization
Option B is correct because a production variant endpoint with auto-scaling based on CPU utilization allows the SageMaker endpoint to dynamically adjust the number of instances in response to traffic spikes, ensuring availability during business hours while minimizing cost by scaling down during off-peak periods. This approach is ideal for a scikit-learn model, which is CPU-bound, making CPU utilization a relevant and effective scaling metric.
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 SageMaker Serverless Inference
Why it's wrong here
Serverless may have cold starts and concurrency limits affecting availability.
✓
Use a production variant endpoint with auto-scaling based on CPU utilization
Why this is correct
Auto-scaling handles traffic spikes efficiently.
Clue confirmation
The clue word "minimum / minimize" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
✗
Use a multi-model endpoint with a single instance type
Why it's wrong here
Multi-model endpoint does not auto-scale for traffic spikes.
✗
Deploy a single large instance that can handle peak load
Why it's wrong here
A single large instance is costly and may be underutilized.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often confuse serverless inference with cost optimization for predictable spikes, overlooking that auto-scaling with a relevant metric like CPU utilization provides both cost efficiency and availability for scheduled traffic patterns.
Detailed technical explanation
How to think about this question
Under the hood, SageMaker auto-scaling uses Application Auto Scaling with target tracking policies based on a predefined metric like CPU utilization (e.g., target value of 70%). The endpoint registers a scaling policy with CloudWatch alarms, which trigger scale-out events when CPU utilization exceeds the target for a sustained period (e.g., 5 minutes) and scale-in events when it drops below. In a real-world scenario, a scikit-learn model processing real-time inference requests during business hours would see CPU utilization spike, prompting the addition of instances, while evenings and weekends would see scale-in, reducing costs.
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 production variant endpoint with auto-scaling based on CPU utilization — Option B is correct because a production variant endpoint with auto-scaling based on CPU utilization allows the SageMaker endpoint to dynamically adjust the number of instances in response to traffic spikes, ensuring availability during business hours while minimizing cost by scaling down during off-peak periods. This approach is ideal for a scikit-learn model, which is CPU-bound, making CPU utilization a relevant and effective scaling metric.
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.
Are there clue words in this question I should notice?
Yes — watch for: "minimum / minimize". Asks for the least resource use — fewest addresses, smallest subnet, lowest overhead. Eliminate over-provisioned options even if they would technically work.
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 startup is deploying a machine learning model for real-time recommendation on Amazon SageMaker. The model is a TensorFlow model (1 GB) and the endpoint uses a single ml.c5.2xlarge instance. The inference latency is currently 500 ms per request. The startup expects traffic to increase 10x in the next month. They want to maintain latency under 500 ms. What is the most cost-effective solution?
easy
A.Use SageMaker Batch Transform to process requests in batches
B.Switch to a GPU instance type for faster inference
✓ C.Set up auto-scaling for the endpoint based on average latency or request count
D.Upgrade to a larger CPU instance type, such as ml.c5.4xlarge
Why C: Option A is correct because auto-scaling adds instances only when needed, handling increased traffic while keeping latency low. Option B (larger instance) is more expensive and may not be needed. Option C (GPU) is overkill and costly. Option D (batch) is not real-time.
Last reviewed: Jun 24, 2026
Question Discussion
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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.
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