Question 560 of 1,672
MLS-C01 Practice Question: Machine Learning Implementation and Operations
A data science team is deploying a machine learning model to production using Amazon SageMaker. The model requires real-time inference with low latency. Which SageMaker feature should they use to deploy the model?
⚠ Common exam trap
A common mix-up: candidates confuse SageMaker Batch Transform (designed for batch processing) with real-time inference, or they mistakenly think SageMaker Notebook Instances can serve as production endpoints because they can run code interactively.
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
✓
SageMaker Realtime Endpoint
SageMaker Realtime Endpoints are designed for low-latency, synchronous inference, making them the correct choice for serving predictions in real time. They keep the model loaded and ready to respond to individual requests, which is essential for applications requiring immediate responses.
Answer analysis
Option-by-option breakdown
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
Notebooks are for development, not serving endpoints.
- ✗
SageMaker Batch Transform
Why it's wrong here
Batch Transform is for offline predictions, not real-time.
- ✗
SageMaker Autopilot
Why it's wrong here
Autopilot automates model building, not deployment endpoints.
- ✓
SageMaker Realtime Endpoint
Why this is correct
Provides low-latency, real-time inference.
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Last reviewed: Jul 4, 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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