The answer is to increase the number of instances and enable auto-scaling. This is the correct step because the logs confirm low model inference time, meaning the bottleneck is not compute speed but queuing—the endpoint simply cannot process incoming requests fast enough, causing requests to pile up and eventually time out with 502 errors. By scaling out, you add more parallel workers to handle the load, directly reducing queuing latency. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this tests your understanding of real-time inference architecture versus model optimization; a common trap is assuming you must optimize the container or switch to batch transform when the issue is throughput, not inference speed. Remember the key distinction: if inference time is low but response time is high, think queue depth, not model speed. A useful memory tip is “low inference, high latency? Scale out, don’t optimize.”
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 has a real-time inference endpoint on Amazon SageMaker that uses a custom container. The endpoint is experiencing high latency and occasional 502 errors. The logs from the container show that the model inference time is low, but the overall response time is high. Which step is MOST likely to reduce the latency?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue: "most likely"
Why it matters: Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Increase the number of instances and enable auto-scaling
Option C is correct because increasing the endpoint's instance count and enabling auto-scaling can distribute the load and reduce queuing delays. Option A is wrong because the inference time is already low, so optimizing the model further won't help much. Option B is wrong because increasing instance size may help but is less cost-effective than scaling out. Option D is wrong because switching to batch transform is for offline inference, not real-time.
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.
✗
Switch to batch transform to process requests in batches
Why it's wrong here
Batch transform is not for real-time inference.
✗
Use a larger instance type for the endpoint
Why it's wrong here
Larger instances may help but scaling out is often more effective for handling concurrent requests.
✗
Optimize the model to reduce inference time
Why it's wrong here
Inference time is already low, so this won't reduce overall latency.
✓
Increase the number of instances and enable auto-scaling
Why this is correct
More instances can handle more concurrent requests, reducing queuing and latency.
Clue confirmation
The clue word "most likely" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
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.
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: Increase the number of instances and enable auto-scaling — Option C is correct because increasing the endpoint's instance count and enabling auto-scaling can distribute the load and reduce queuing delays. Option A is wrong because the inference time is already low, so optimizing the model further won't help much. Option B is wrong because increasing instance size may help but is less cost-effective than scaling out. Option D is wrong because switching to batch transform is for offline inference, not real-time.
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: "most likely". Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.
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 host a model that performs real-time inference. The model receives around 100 requests per second with occasional spikes up to 500 requests per second. The current endpoint uses 2 ml.m5.large instances. During spikes, latency increases significantly, and some requests time out. What is the MOST cost-effective solution to handle the spikes without losing requests?
hard
A.Replace the instances with a single larger instance type, such as ml.m5.4xlarge
B.Use an Amazon SQS queue to buffer incoming requests and process them asynchronously
C.Use AWS Lambda with a provisioned concurrency to handle the spikes
✓ D.Configure SageMaker managed scaling with a target tracking policy and add a buffer based on the average spike duration
Why D: Option C is correct because adding a buffer to the autoscaling policy allows the endpoint to scale proactively before the spike fully hits, while managed scaling adjusts instances based on demand. Option A (increase instance size) is less cost-effective than scaling out. Option B (SQS) adds latency. Option D (Lambda) may not be suitable for real-time inference.
Last reviewed: Jun 20, 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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