- A
Reduce the number of instances to zero during off-peak hours and manually launch a new endpoint every day at 12:00
Why wrong: Manual intervention is error-prone and causes downtime during scaling.
- B
Switch to SageMaker Batch Transform and have the application send requests in batches
Why wrong: Batch Transform is for offline predictions, not real-time.
- C
Configure SageMaker endpoint auto scaling with a target CPU utilization of 70% and a minimum instance count of 1
Auto scaling dynamically adjusts instance count to handle load, keeping latency low and cost efficient.
- D
Replace the endpoint instance type with ml.m5.4xlarge to handle peak load
Why wrong: Vertical scaling is less cost-effective than horizontal scaling, as it runs a large instance all the time.
Quick Answer
The answer is to configure SageMaker endpoint auto scaling with a target CPU utilization of 70% and a minimum instance count of 1. This solution directly addresses the latency spike by dynamically adding instances when CPU utilization exceeds the target threshold during peak hours, then scaling back down to a single instance when demand drops, thereby maintaining latency under 500 ms while controlling cost. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this scenario tests your understanding of real-time inference optimization versus batch processing—a common trap is confusing SageMaker’s auto scaling with manual instance resizing or Batch Transform, which is for offline jobs. Remember the key principle: for variable traffic on a real-time endpoint, always pair a CPU utilization target with a minimum instance count to balance performance and cost. Memory tip: “CPU target plus min count keeps latency down and wallet sound.”
MLS-C01 Practice Question: Machine Learning Implementation and Operations
This MLS-C01 practice question tests your understanding of machine learning implementation and operations. Examine the command output carefully: the correct answer depends on what the output actually shows, not on general recall alone. 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 is using Amazon SageMaker to train and deploy a fraud detection model. The model is a gradient boosting machine (GBM) trained on a dataset with 10 million rows and 50 features. The training job runs on an ml.m5.2xlarge instance with 8 vCPUs and 32 GB memory. The training completes successfully, and the model is deployed to a real-time endpoint. After deployment, the inference latency is around 200 ms per request, which is acceptable. However, after a week, the company observes that latency increases to over 1 second during peak hours (12:00-13:00 UTC). CloudWatch metrics show CPU utilization on the endpoint instance reaches 95% during these peaks. The endpoint is configured with a single ml.m5.large instance. The company wants to maintain latency under 500 ms during peak hours without incurring unnecessary cost during off-peak hours. Which solution should the company implement?
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
Configure SageMaker endpoint auto scaling with a target CPU utilization of 70% and a minimum instance count of 1
Option A is correct: configuring auto scaling based on CPU utilization adds instances during peak and removes them during off-peak, meeting latency and cost goals. Option B (Batch Transform) is for offline inference. Option C (larger instance) is less cost-effective. Option D (scale down) would worsen latency.
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.
- ✗
Reduce the number of instances to zero during off-peak hours and manually launch a new endpoint every day at 12:00
Why it's wrong here
Manual intervention is error-prone and causes downtime during scaling.
- ✗
Switch to SageMaker Batch Transform and have the application send requests in batches
Why it's wrong here
Batch Transform is for offline predictions, not real-time.
- ✓
Configure SageMaker endpoint auto scaling with a target CPU utilization of 70% and a minimum instance count of 1
Why this is correct
Auto scaling dynamically adjusts instance count to handle load, keeping latency low and cost efficient.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Replace the endpoint instance type with ml.m5.4xlarge to handle peak load
Why it's wrong here
Vertical scaling is less cost-effective than horizontal scaling, as it runs a large instance all the time.
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.
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Machine Learning Implementation and Operations — study guide chapter
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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: Configure SageMaker endpoint auto scaling with a target CPU utilization of 70% and a minimum instance count of 1 — Option A is correct: configuring auto scaling based on CPU utilization adds instances during peak and removes them during off-peak, meeting latency and cost goals. Option B (Batch Transform) is for offline inference. Option C (larger instance) is less cost-effective. Option D (scale down) would worsen latency.
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 →
Same concept, more angles
1 more ways this is tested on MLS-C01
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 company is using Amazon SageMaker to deploy a real-time inference endpoint for a computer vision model. The endpoint receives bursts of traffic with up to 500 requests per second, but the load is unpredictable. Which scaling strategy is MOST cost-effective while maintaining low latency?
medium- A.Manually provision enough instances to handle peak load
- B.Use provisioned concurrency on SageMaker Serverless Inference
- C.Use a multi-model endpoint to reduce the number of instances
- ✓ D.Configure automatic scaling with a target tracking policy and add a buffer to handle bursts
Why D: Option C is correct because SageMaker can add instances in response to increased load, and using a buffer helps absorb sudden spikes. Option A (provisioned concurrency) is for serverless but not SageMaker. Option B (manual scaling) is not cost-effective for unpredictable traffic. Option D (multi-model endpoints) is for serving multiple models, not for scaling.
Last reviewed: Jun 20, 2026
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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