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 company wants to deploy a machine learning model that requires very low latency predictions (under 10ms). The model is a small ensemble of decision trees. Which SageMaker deployment option is most suitable?
SageMaker Notebook instance
Why wrong: For development, not production hosting.
AWS Lambda function with the model packaged
Why wrong: Cold starts and not optimized for low latency.
SageMaker endpoint with a single instance
Provides real-time low-latency inference.
SageMaker Batch Transform
Why wrong: For offline predictions, not low latency.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
SageMaker endpoint with a single instance
C is correct because a SageMaker endpoint with a single instance provides a persistent, real-time inference API that can achieve sub-10ms latency for a small ensemble of decision trees. The endpoint keeps the model loaded in memory and uses synchronous HTTP requests, minimizing cold start and network overhead, which is essential for low-latency predictions.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
For each option: why learners choose it and why it is or isn't the right answer here.
SageMaker Notebook instance
Why it's wrong here
For development, not production hosting.
AWS Lambda function with the model packaged
Why it's wrong here
Cold starts and not optimized for low latency.
SageMaker endpoint with a single instance
Why this is correct
Provides real-time low-latency inference.
Related concept
Read the scenario before looking for a memorised answer.
SageMaker Batch Transform
Why it's wrong here
For offline predictions, not low latency.
Common exam traps
The trap here is that candidates often confuse batch processing (Batch Transform) with real-time inference, or assume that serverless options like Lambda are always the fastest, ignoring cold start and timeout constraints.
Detailed technical explanation
Under the hood, a SageMaker endpoint uses an Amazon Elastic Compute Cloud (EC2) instance running a Docker container with a pre-loaded inference server (e.g., using the SageMaker Inference Toolkit). For a small ensemble of decision trees, the model size is typically a few megabytes, so inference can complete in microseconds, with the remaining latency dominated by network round-trip time (RTT) and serialization/deserialization overhead. In a real-world scenario, if the model were a large deep neural network, a single instance might not suffice, but for small ensembles, a single ml.m5.large instance can easily meet the 10ms requirement.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
A healthcare organisation deploys an application with a public-facing web tier and a private database tier. The database subnet has no public IP and only accepts connections from the web tier's security group. Questions like this test whether you can design cloud network isolation using VNets/VPCs, subnets, and security group rules.
What to study next
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FAQ
Modeling — This question tests Modeling — Read the scenario before looking for a memorised answer..
The correct answer is: SageMaker endpoint with a single instance — C is correct because a SageMaker endpoint with a single instance provides a persistent, real-time inference API that can achieve sub-10ms latency for a small ensemble of decision trees. The endpoint keeps the model loaded in memory and uses synchronous HTTP requests, minimizing cold start and network overhead, which is essential for low-latency predictions.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 24, 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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