Quick Answer
The answer is SageMaker Multi-model endpoint. This hosting option is most suitable because it allows you to deploy a large ensemble of decision trees on a single endpoint while dynamically loading and caching models in memory, which keeps inference latency well under 100ms by avoiding cold starts for frequently accessed models. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this question tests your understanding of how to balance cost and performance for real-time inference, often presenting SageMaker real-time endpoints as a trap due to their higher cost per model. The key distinction is that a multi-model endpoint shares compute resources across multiple models, making it ideal for large ensembles where individual model sizes are moderate but the total number of models is high. Memory tip: think “multi-model = multiple models, one endpoint, low latency through caching.”
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 model for real-time fraud detection. The model must make predictions with less than 100ms latency. The team uses SageMaker and the model is a large ensemble of decision trees. Which SageMaker hosting option is MOST suitable?
SageMaker Multi-model endpoint
Supports multiple models on one instance with low latency.
SageMaker Serverless Inference
Why wrong: Cold starts may increase latency beyond 100ms.
SageMaker Elastic Inference
Why wrong: Elastic Inference is for deep learning models, not tree ensembles.
SageMaker Batch Transform
Why wrong: Batch Transform is for offline, not real-time.
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 Multi-model endpoint
A SageMaker Multi-model endpoint is the most suitable option because it allows hosting multiple models (including large ensembles) on a single endpoint while sharing underlying compute resources, which reduces cost and latency. The endpoint dynamically loads and caches models based on inference requests, enabling sub-100ms predictions for large ensemble models by keeping frequently used models in memory.
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 Multi-model endpoint
Why this is correct
Supports multiple models on one instance with low latency.
Related concept
Read the scenario before looking for a memorised answer.
SageMaker Serverless Inference
Why it's wrong here
Cold starts may increase latency beyond 100ms.
SageMaker Elastic Inference
Why it's wrong here
Elastic Inference is for deep learning models, not tree ensembles.
SageMaker Batch Transform
Why it's wrong here
Batch Transform is for offline, not real-time.
Common exam traps
The trap here is that candidates might choose SageMaker Serverless Inference (Option B) thinking it automatically handles scaling for real-time workloads, but they overlook the cold start latency penalty that makes it unsuitable for sub-100ms latency requirements.
Detailed technical explanation
SageMaker Multi-model endpoints use a model cache on the instance's local SSD or EBS volume, and models are loaded on-demand via a custom inference container that handles routing. The endpoint can serve thousands of models per instance, and the cache eviction policy (LRU) ensures that hot models remain in memory, minimizing latency for frequent inference requests. In practice, for a large ensemble of decision trees, the model size can be kept under 1 GB per model to avoid excessive disk I/O, and the ensemble can be serialized as a single model object to reduce loading overhead.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
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
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FAQ
Modeling — This question tests Modeling — Read the scenario before looking for a memorised answer..
The correct answer is: SageMaker Multi-model endpoint — A SageMaker Multi-model endpoint is the most suitable option because it allows hosting multiple models (including large ensembles) on a single endpoint while sharing underlying compute resources, which reduces cost and latency. The endpoint dynamically loads and caches models based on inference requests, enabling sub-100ms predictions for large ensemble models by keeping frequently used models in memory.
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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