Question 201 of 1,672
SageMaker Real-Time Endpoint for Low Latency Inference
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?
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.
⚠ Common exam trap
Many exam-takers 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.
Answer choices
Why each option matters
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.
Answer analysis
Option-by-option breakdown
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.
- ✗
Multi-model endpoint
Why it's wrong here
Multi-model may add overhead.
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Same concept, more angles
3 more ways this is tested on MLS-C01
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?
easy- ✓ A.Amazon SageMaker real-time endpoint
- B.Amazon SageMaker batch transform
- C.Amazon EC2 with auto scaling
- D.AWS Lambda with GPU
Why 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?
easy- A.Amazon EMR with Spark Streaming
- B.AWS Glue
- ✓ C.Amazon SageMaker endpoint
- D.AWS Lambda with custom container
Why 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?
easy- A.AWS Glue
- B.AWS Lambda with a container image
- ✓ C.Amazon SageMaker real-time endpoint
- D.Amazon EMR
Why C: Amazon SageMaker real-time endpoints are purpose-built for low-latency inference and can host large models like this 500 MB deep neural network by using appropriate instance types or multi-model endpoints. Option A (AWS Glue) is an ETL service, not for real-time inference. Option B (AWS Lambda) has a 250 MB deployment package limit and cold start latency that would exceed the 100 ms requirement for a 500 MB model. Option D (Amazon EMR) is designed for big data processing with Hadoop/Spark, not for real-time predictions. Therefore, the correct choice is Amazon SageMaker real-time endpoint.
Last reviewed: Jun 24, 2026
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