- A
Use ml.p3 instances with a multi-model endpoint.
Why wrong: Incorrect. ml.p3 instances are GPU-optimized and more expensive; multi-model endpoints are useful for deploying multiple models but the cost and latency may not be optimal for a single model.
- B
Use AWS Lambda with a container image for serverless inference.
Why wrong: Incorrect. AWS Lambda can have cold starts exceeding 100 ms, and serverless inference is not ideal for steady-state traffic with spikes; also, Lambda is not a SageMaker deployment strategy.
- C
Use ml.m5 instances with a production variant and auto-scaling.
Why wrong: Incorrect. ml.m5 instances are general-purpose but may be less cost-effective for compute-heavy inference; auto-scaling without a minimum instance count risks scaling latency during spikes.
- D
Use ml.c5 instances with a single endpoint and auto-scaling with a minimum instance count to handle baseline traffic.
Correct. ml.c5 instances provide compute-optimized performance at lower cost, and auto-scaling with a minimum instance count ensures pre-warmed capacity for low latency.
MLS-C01 Amazon SageMaker real-time endpoints 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. A key principle to apply: amazon SageMaker real-time endpoints. 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 team is deploying a real-time inference endpoint for a fraud detection model using Amazon SageMaker. The model requires low latency (<100 ms) and the team expects a steady stream of requests with occasional spikes. Which instance type and deployment strategy should they use to minimize cost while meeting latency requirements?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue:
"minimum / minimize"Why it matters: Asks for the least resource use — fewest addresses, smallest subnet, lowest overhead. Eliminate over-provisioned options even if they would technically work.
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 ml.c5 instances with a single endpoint and auto-scaling with a minimum instance count to handle baseline traffic.
Option D is correct because ml.c5 instances are compute-optimized for low-latency inference, and auto-scaling with a minimum instance count ensures pre-warmed capacity to handle steady traffic with spikes without cold starts, meeting the <100 ms requirement cost-effectively. This combination avoids over-provisioning while ensuring consistent performance.
Key principle: Amazon SageMaker real-time endpoints
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 ml.p3 instances with a multi-model endpoint.
Why it's wrong here
Incorrect. ml.p3 instances are GPU-optimized and more expensive; multi-model endpoints are useful for deploying multiple models but the cost and latency may not be optimal for a single model.
- ✗
Use AWS Lambda with a container image for serverless inference.
Why it's wrong here
Incorrect. AWS Lambda can have cold starts exceeding 100 ms, and serverless inference is not ideal for steady-state traffic with spikes; also, Lambda is not a SageMaker deployment strategy.
- ✗
Use ml.m5 instances with a production variant and auto-scaling.
Why it's wrong here
Incorrect. ml.m5 instances are general-purpose but may be less cost-effective for compute-heavy inference; auto-scaling without a minimum instance count risks scaling latency during spikes.
- ✓
Use ml.c5 instances with a single endpoint and auto-scaling with a minimum instance count to handle baseline traffic.
Why this is correct
Correct. ml.c5 instances provide compute-optimized performance at lower cost, and auto-scaling with a minimum instance count ensures pre-warmed capacity for low latency.
Clue confirmation
The clue word "minimum / minimize" in the question point toward this answer.
Related concept
Amazon SageMaker real-time endpoints
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often choose auto-scaling without a minimum instance count (Option C) thinking it handles spikes cost-effectively, but they overlook the latency penalty of scaling up during a spike, which can exceed 100 ms. With a minimum instance count set to handle baseline traffic, scaling adds capacity during spikes with minimal latency impact.
Detailed technical explanation
How to think about this question
Provisioned concurrency in SageMaker keeps a specified number of instances warm and ready to serve requests, eliminating cold start latency that can occur with auto-scaling. The ml.c5 instance family uses Intel Xeon Scalable processors with high clock speeds and AVX-512 instructions, which accelerate inference for models like XGBoost or linear classifiers commonly used in fraud detection. In practice, a steady stream of requests with spikes benefits from provisioned concurrency because it pre-allocates capacity, while auto-scaling (Option C) would lag behind sudden traffic increases due to the time required to launch new instances.
KKey Concepts to Remember
- Amazon SageMaker real-time endpoints
- Auto-scaling with minimum instances
- Compute-optimized instances (ml.c5)
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
Amazon SageMaker real-time endpoints
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.
Review amazon SageMaker real-time endpoints, then practise related MLS-C01 questions on the same topic to reinforce the concept.
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FAQ
Questions learners often ask
What does this MLS-C01 question test?
Modeling — This question tests Modeling — Amazon SageMaker real-time endpoints.
What is the correct answer to this question?
The correct answer is: Use ml.c5 instances with a single endpoint and auto-scaling with a minimum instance count to handle baseline traffic. — Option D is correct because ml.c5 instances are compute-optimized for low-latency inference, and auto-scaling with a minimum instance count ensures pre-warmed capacity to handle steady traffic with spikes without cold starts, meeting the <100 ms requirement cost-effectively. This combination avoids over-provisioning while ensuring consistent performance.
What should I do if I get this MLS-C01 question wrong?
Review amazon SageMaker real-time endpoints, then practise related MLS-C01 questions on the same topic to reinforce the concept.
Are there clue words in this question I should notice?
Yes — watch for: "minimum / minimize". Asks for the least resource use — fewest addresses, smallest subnet, lowest overhead. Eliminate over-provisioned options even if they would technically work.
What is the key concept behind this question?
Amazon SageMaker real-time endpoints
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Last reviewed: Jun 24, 2026
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