The answer is to use Amazon SageMaker Serverless Inference for bursty small requests. This deployment option is correct because it automatically scales from zero to handle traffic spikes, charges only for the compute time consumed per millisecond, and incurs zero idle costs—perfectly matching the pattern of small, intermittent payloads where a provisioned instance would waste money between bursts. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this question tests your understanding of cost-optimized inference architectures; a common trap is choosing a real-time endpoint with auto-scaling, which still incurs a base cost for the underlying instances even at low traffic. Remember the key trade-off: serverless excels for unpredictable, short-lived workloads, while provisioned endpoints suit steady, high-throughput traffic. For the exam, think “bursty and small? Serverless handles it all.”
MLS-C01 Modeling Practice Question
This MLS-C01 practice question tests your understanding of modeling. Compare every option against the stated constraints before choosing — the best answer satisfies all requirements, not just the most obvious one. 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 is using Amazon SageMaker to deploy a model for real-time inference. The model receives requests that are small but arrive in bursts. The data scientist wants to minimize latency and cost. Which deployment option is MOST appropriate?
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 Amazon SageMaker Serverless Inference
Amazon SageMaker Serverless Inference is the most appropriate option because it automatically scales compute resources based on request volume, charges only for the compute time used during inference (per-millisecond billing), and has no idle costs. This matches the bursty, small-request pattern perfectly, minimizing both latency and cost without requiring manual instance management.
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 a real-time endpoint with a single instance
Why it's wrong here
Single instance may not handle bursts and incurs cost even when idle.
✗
Use a multi-model endpoint with auto-scaling
Why it's wrong here
Multi-model endpoints are for hosting multiple models, not specifically for burst handling.
✓
Use Amazon SageMaker Serverless Inference
Why this is correct
Serverless scales automatically and you pay only for inference duration.
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.
✗
Use a batch transform job triggered by a schedule
Why it's wrong here
Batch transform is for offline predictions, not real-time inference.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often confuse 'multi-model endpoints' with 'serverless' and assume auto-scaling eliminates idle costs, but multi-model endpoints still require a minimum number of running instances, incurring continuous charges.
Detailed technical explanation
How to think about this question
SageMaker Serverless Inference uses AWS Lambda-like infrastructure under the hood, with a maximum concurrency limit per endpoint (default 50, adjustable via quota) and a maximum payload size of 6 MB. It automatically scales from zero to thousands of concurrent invocations within seconds, making it ideal for spiky workloads, but it has a cold-start latency of a few seconds for the first request after idle periods, which is acceptable for small bursts. Real-world scenarios include chatbots or IoT sensor predictions where traffic is intermittent and unpredictable.
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 Amazon SageMaker Serverless Inference — Amazon SageMaker Serverless Inference is the most appropriate option because it automatically scales compute resources based on request volume, charges only for the compute time used during inference (per-millisecond billing), and has no idle costs. This matches the bursty, small-request pattern perfectly, minimizing both latency and cost without requiring manual instance management.
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.
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 deploying a fraud detection model using Amazon SageMaker. The model is a linear learner trained on 100 GB of data. For inference, the model receives individual transactions and must return a prediction within 100 ms. Which endpoint configuration should the team use to meet the latency requirement?
medium
A.Use a multi-model endpoint with CPU instances.
✓ B.Deploy a single model endpoint using a GPU instance and enable autoscaling.
C.Use a batch transform job scheduled every minute.
D.Deploy using SageMaker Serverless Inference.
Why B: Option B is correct because a single-model endpoint on a GPU instance provides the low-latency, high-throughput inference required for real-time fraud detection. GPU instances accelerate linear learner inference by parallelizing matrix operations, enabling sub-100 ms predictions for individual transactions. Autoscaling ensures the endpoint can handle traffic spikes without degrading latency.
Last reviewed: Jun 24, 2026
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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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