- A
Deploy the model on a single endpoint with automatic scaling based on CPU utilization.
Why wrong: Auto Scaling based on CPU may not be enough to guarantee low latency; it also requires correct instance selection.
- B
Use SageMaker Serverless Inference with provisioned concurrency.
Why wrong: Serverless Inference has a maximum concurrency of 200 per endpoint and may not achieve 1000 TPS with low latency.
- C
Use SageMaker Inference Recommender to find the optimal instance type and endpoint configuration.
Inference Recommender runs load tests and suggests the best instance and configuration to meet latency and throughput targets.
- D
Use a multi-model endpoint to load multiple copies of the model on the same instance.
Why wrong: Multi-model endpoints are for multiple distinct models, not for scaling a single large model efficiently.
Quick Answer
The answer is to use SageMaker Inference Recommender to find the optimal instance type and endpoint configuration. This tool directly addresses the need to balance low latency under 100 ms with high throughput of 1000 requests per second by running automated load tests against your model, then recommending the best instance and endpoint settings. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this scenario tests your understanding that Inference Recommender is purpose-built for performance tuning, unlike Multi-Model Endpoints (which share resources across many small models) or Serverless Inference (which caps concurrency and may not sustain 1000 TPS). A common trap is assuming Auto Scaling alone solves performance issues—but scaling a poorly chosen instance still fails to meet latency targets. Remember the mnemonic: “If latency and throughput are the quest, let Inference Recommender do the rest.”
MLS-C01 Practice Question: Machine Learning Implementation and Operations
This MLS-C01 practice question tests your understanding of machine learning implementation and operations. 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 machine learning engineer is deploying a real-time inference endpoint using Amazon SageMaker. The model is a large deep learning model that requires low latency (under 100 ms) and high throughput (1000 requests per second). Which SageMaker deployment option is MOST suitable?
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
Use SageMaker Inference Recommender to find the optimal instance type and endpoint configuration.
Option C is correct because SageMaker Inference Recommender provides automated testing and recommendations for instance type and configuration to meet latency and throughput requirements. Option A is wrong because Multi-Model Endpoints are designed for multiple small models, not optimized for a single large model's throughput. Option B is wrong because Serverless Inference has a maximum concurrency and may not achieve 1000 TPS with low latency. Option D is wrong because a single endpoint may not handle the load; Auto Scaling helps but does not guarantee optimal instance choice.
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.
- ✗
Deploy the model on a single endpoint with automatic scaling based on CPU utilization.
Why it's wrong here
Auto Scaling based on CPU may not be enough to guarantee low latency; it also requires correct instance selection.
- ✗
Use SageMaker Serverless Inference with provisioned concurrency.
Why it's wrong here
Serverless Inference has a maximum concurrency of 200 per endpoint and may not achieve 1000 TPS with low latency.
- ✓
Use SageMaker Inference Recommender to find the optimal instance type and endpoint configuration.
Why this is correct
Inference Recommender runs load tests and suggests the best instance and configuration to meet latency and throughput targets.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Use a multi-model endpoint to load multiple copies of the model on the same instance.
Why it's wrong here
Multi-model endpoints are for multiple distinct models, not for scaling a single large model efficiently.
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.
- →
Machine Learning Implementation and Operations — study guide chapter
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Machine Learning Implementation and Operations practice questions
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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: Use SageMaker Inference Recommender to find the optimal instance type and endpoint configuration. — Option C is correct because SageMaker Inference Recommender provides automated testing and recommendations for instance type and configuration to meet latency and throughput requirements. Option A is wrong because Multi-Model Endpoints are designed for multiple small models, not optimized for a single large model's throughput. Option B is wrong because Serverless Inference has a maximum concurrency and may not achieve 1000 TPS with low latency. Option D is wrong because a single endpoint may not handle the load; Auto Scaling helps but does not guarantee optimal instance choice.
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
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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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