Quick Answer
The answer is the SageMaker real-time endpoint. This is the correct choice because real-time endpoints are specifically designed for low latency inference, typically under 10 milliseconds, by keeping the model persistently loaded and ready to respond to individual prediction requests via a dedicated HTTPS endpoint. For a small ensemble of decision trees, which is lightweight and requires minimal compute, the real-time endpoint provides the necessary speed without the overhead of batch processing or the cold-start delays of serverless inference. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this question tests your ability to match deployment configurations to latency requirements, often using a trap where candidates confuse real-time endpoints with batch transform or asynchronous inference—remember that batch is for offline processing, not sub-10 ms responses. A helpful memory tip: think “real-time = ready and waiting,” as the endpoint keeps the model warm to deliver predictions in a flash.
This MLS-C01 practice question tests your understanding of modeling. This is a configuration task: choose the command set that satisfies every stated requirement. Small differences — like 'secret' vs 'password' or 'transport input ssh' vs 'all' — change whether the answer is correct. 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 engineer needs to deploy a model that requires low latency (under 10 ms) for real-time inference. The model is a small ensemble of decision trees. Which Amazon SageMaker endpoint configuration is MOST appropriate?
Batch transform
Why wrong: Batch is not real-time.
Training job
Why wrong: Training is not for inference.
Real-time endpoint
Real-time endpoints provide low latency.
Multi-model endpoint
Why wrong: Multi-model may add overhead.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
Real-time endpoint
Real-time endpoints in Amazon SageMaker are designed for low-latency inference (typically under 10 ms) and are the correct choice for deploying a small ensemble of decision trees that needs to respond to individual prediction requests in real time. They keep the model loaded and ready, providing a persistent HTTPS endpoint that can serve predictions with minimal overhead.
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.
Batch transform
Why it's wrong here
Batch is not real-time.
Training job
Why it's wrong here
Training is not for inference.
Real-time endpoint
Why this is correct
Real-time endpoints provide low latency.
Related concept
Read the scenario before looking for a memorised answer.
Multi-model endpoint
Why it's wrong here
Multi-model may add overhead.
Common exam traps
The trap here is that candidates often confuse Multi-model endpoints with real-time endpoints, assuming they offer the same low-latency guarantees, but Multi-model endpoints trade off latency for cost efficiency by loading models on demand, which can introduce delays that violate strict latency requirements.
Detailed technical explanation
Under the hood, a SageMaker real-time endpoint uses an auto-scaling group of EC2 instances running the model container, with an Application Load Balancer (ALB) distributing requests. For a small ensemble of decision trees, the inference payload is typically small (e.g., a few KB), and the model can be serialized using libraries like XGBoost or scikit-learn, allowing inference in under 1 ms on a CPU instance. A real-world scenario where this matters is a fraud detection system that must score each transaction in real time; using a multi-model endpoint could cause occasional cold-start delays exceeding 10 ms when a less frequently used model is loaded.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
A cloud solutions architect for a retail company is evaluating services for a new workload. The correct answer here reflects best practice for the specific scenario described — not a general cloud recommendation. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Cloud exam questions reward reading the constraint carefully: the same technology can be right or wrong depending on the use case.
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: Real-time endpoint — Real-time endpoints in Amazon SageMaker are designed for low-latency inference (typically under 10 ms) and are the correct choice for deploying a small ensemble of decision trees that needs to respond to individual prediction requests in real time. They keep the model loaded and ready, providing a persistent HTTPS endpoint that can serve predictions with minimal overhead.
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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Same concept, more angles
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 machine learning engineer needs to deploy a real-time inference endpoint for a model that requires GPU acceleration for low latency. Which AWS service should be used?
easyWhy A: Amazon SageMaker provides real-time endpoints that support GPU instances for low-latency inference. AWS Lambda does not support GPU, and Batch is for asynchronous processing. EC2 would require manual management.
Variation 2. A machine learning engineer needs to deploy a model that makes real-time predictions with latency under 100ms. The model is a small ensemble of decision trees. Which AWS service is MOST suitable?
easyWhy C: Amazon SageMaker provides real-time endpoints with low latency for model inference, and can host the ensemble as a single endpoint.
Variation 3. A machine learning team needs to deploy a model that makes real-time predictions with latency under 100 ms. The model is a deep neural network with 500 MB of parameters. Which AWS service should they use?
easyWhy C: Amazon SageMaker real-time endpoints are designed for low-latency inference. Option B is wrong because AWS Lambda has a 250 MB deployment package limit and higher latency for large models. Option C is wrong because Amazon EMR is for big data processing, not real-time inference. Option D is wrong because AWS Glue is for ETL jobs.
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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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