- 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.
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 runs load tests against the model to identify the optimal instance type, instance count, and endpoint configuration (e.g., container parameters, model server settings) that meet the specific latency and throughput requirements. For a large deep learning model demanding under 100 ms latency and 1000 requests per second, this automated benchmarking is essential to avoid over-provisioning or under-provisioning resources.
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
The trap here is that candidates assume serverless or multi-model endpoints are always cost-effective for high throughput, but they fail to account for the strict latency and concurrency ceilings that make those options unsuitable for demanding real-time inference workloads.
Detailed technical explanation
How to think about this question
SageMaker Inference Recommender uses a Bayesian optimization approach to explore instance families (e.g., GPU-accelerated instances like ml.p3 or ml.g5) and endpoint configurations, measuring metrics such as p99 latency and transactions per second under simulated production traffic. Under the hood, it deploys the model to a temporary endpoint, runs a configurable number of requests with varying payload sizes, and returns a recommendation that includes instance count, initial instance count, and scaling policies. In practice, this tool can also detect bottlenecks like insufficient GPU memory or CPU-bound preprocessing that manual tuning would miss.
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 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 exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
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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 runs load tests against the model to identify the optimal instance type, instance count, and endpoint configuration (e.g., container parameters, model server settings) that meet the specific latency and throughput requirements. For a large deep learning model demanding under 100 ms latency and 1000 requests per second, this automated benchmarking is essential to avoid over-provisioning or under-provisioning resources.
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.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Last reviewed: Jul 4, 2026
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