The answer is to configure auto scaling based on the number of active invocations to maintain a buffer of warmed instances. This approach directly addresses the cold start latency issue by ensuring that SageMaker’s managed warm pools always have pre-loaded instances ready to handle traffic spikes, preventing new instances from suffering the 2-second model load time. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this scenario tests your understanding of how to balance cost and performance for real-time endpoints, often trapping candidates who choose larger instances (which still cold start) or Serverless Inference (which has its own cold start latency). The key insight is that auto scaling with a target metric keeps a proactive buffer, while scaling down aggressively would undo that benefit. Memory tip: think “buffer beats burst” — maintain a warm reserve to absorb sudden spikes.
MLS-C01 Modeling Practice Question
This MLS-C01 practice question tests your understanding of modeling. 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 company uses Amazon SageMaker to host a model for real-time inference. The model is a large ensemble that takes 2 seconds to load into memory. To reduce cold start latency, the data scientist uses SageMaker's managed warm pools. However, they notice that during a sudden traffic spike, new instances still experience high latency. What is the BEST way to ensure consistently low latency for all requests?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue: "best"
Why it matters: Signals that multiple options may be partially correct. Choose the option that most directly solves the exact problem described, not the one that sounds most complete.
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Configure auto scaling based on the number of active invocations to maintain a buffer of warmed instances.
Option C is correct. Using auto scaling with a target metric keeps a buffer of warm instances. Option A is wrong because increasing instance size reduces per-request time but cold start still occurs. Option B is wrong because Serverless Inference has its own cold start issues. Option D is wrong because scaling down aggressively increases cold starts.
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 larger instance type to reduce model loading time.
Why it's wrong here
Larger instances may load faster but cold start still happens on new instances.
✓
Configure auto scaling based on the number of active invocations to maintain a buffer of warmed instances.
Why this is correct
Auto scaling with a buffer ensures that new instances are provisioned ahead of demand, reducing cold start impact.
Clue confirmation
The clue word "best" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
✗
Reduce the number of instances to minimize cold start frequency.
Why it's wrong here
Fewer instances increase the likelihood of cold starts during spikes.
✗
Switch to SageMaker Serverless Inference.
Why it's wrong here
Serverless also has cold starts, especially for large models.
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.
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: Configure auto scaling based on the number of active invocations to maintain a buffer of warmed instances. — Option C is correct. Using auto scaling with a target metric keeps a buffer of warm instances. Option A is wrong because increasing instance size reduces per-request time but cold start still occurs. Option B is wrong because Serverless Inference has its own cold start issues. Option D is wrong because scaling down aggressively increases cold starts.
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.
Are there clue words in this question I should notice?
Yes — watch for: "best". Signals that multiple options may be partially correct. Choose the option that most directly solves the exact problem described, not the one that sounds most complete.
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 company is using Amazon SageMaker to deploy a model for real-time inference. The model has a latency requirement of less than 100 milliseconds. During testing, the latency is around 150 milliseconds. Which action can most likely reduce the latency to meet the requirement?
medium
A.Reduce the batch size for inference.
B.Enable data capture for the endpoint.
C.Increase the initial variant weight for the production variant.
✓ D.Use a larger instance type for the endpoint.
Why D: Enabling data capture adds overhead and increases latency. Using a larger instance type would provide more compute and reduce latency, but may increase cost. Reducing the batch size for inference (if batching is used) can reduce latency because the model processes fewer requests at once. However, the question implies a real-time endpoint which typically processes one request at a time; batch size might be 1. Increasing the variant weight for the production variant is for traffic routing, not latency. The most direct is to use a more powerful instance type. But also consider that increasing batch size (if using multi-record) increases latency. Reducing batch size reduces latency. However, for a real-time endpoint, the instance type is key. I'll go with using a larger instance type.
Variation 2. A company uses Amazon SageMaker to host a model for real-time predictions. The model endpoint is experiencing high latency during peak hours. The data scientist wants to reduce latency without increasing cost. Which action should they take?
easy
A.Enable data capture for the endpoint to log requests
B.Switch to a larger instance type
C.Reduce the number of instances behind the endpoint
✓ D.Enable auto-scaling for the endpoint based on latency metrics
Why D: Using SageMaker's production variants with auto-scaling can help handle traffic spikes without over-provisioning, thus managing latency and cost. Switching to a larger instance would increase cost. Reducing the number of instances would increase latency. Enabling data capture adds overhead and increases latency.
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.
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