The correct choice is ml.c5 instances with a single endpoint and provisioned concurrency. This combination minimizes cost while meeting the sub-100 millisecond latency requirement because ml.c5 instances are compute-optimized for fast model inference, and provisioned concurrency pre-warms a baseline number of instances to handle steady traffic, then scales up elastically during spikes without incurring cold starts. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this scenario tests your understanding of how provisioned concurrency prevents the cost spikes associated with over-provisioning for peak load, while still guaranteeing low latency. A common trap is choosing auto-scaling without provisioned concurrency, which introduces cold-start delays during sudden traffic surges. Memory tip: think “pre-warm to prevent the spike in cost and latency”—provisioned concurrency keeps your endpoint ready for the burst without paying for idle capacity.
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 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 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 provisioned concurrency.
Option D is correct because ml.c5 instances are compute-optimized for low-latency inference, and provisioned concurrency pre-warms the endpoint 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: 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 ml.p3 instances with a multi-model endpoint.
Why it's wrong here
GPU instances are overkill and costly for inference.
✗
Use AWS Lambda with a container image for serverless inference.
Why it's wrong here
Lambda may have cold starts and is not optimized for steady low-latency inference.
✗
Use ml.m5 instances with a production variant and auto-scaling.
Why it's wrong here
ml.m5 are general-purpose, but ml.c5 is more cost-effective for CPU inference.
✓
Use ml.c5 instances with a single endpoint and provisioned concurrency.
Why this is correct
Compute-optimized instances and auto-scaling meet latency and cost goals.
Clue confirmation
The clue word "minimum / minimize" 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
The trap here is that candidates often choose auto-scaling (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, whereas provisioned concurrency (Option D) pre-warms capacity to meet the latency requirement.
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
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 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 exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
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: Use ml.c5 instances with a single endpoint and provisioned concurrency. — Option D is correct because ml.c5 instances are compute-optimized for low-latency inference, and provisioned concurrency pre-warms the endpoint 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?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
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?
Read the scenario before looking for a memorised answer.
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Question Discussion
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