MLS-C01 Practice Question: Machine Learning Implementation and Operations
A company wants to serve predictions from a model using a REST API with low latency. Which SageMaker deployment option is most appropriate?
⚠ Common exam trap
Many exam-takers confuse batch inference (Batch Transform) with real-time inference, or mistakenly think a Notebook instance can serve as a production API, when only the real-time endpoint provides the persistent, low-latency REST API required.
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 real-time endpoint
SageMaker real-time endpoints are designed for low-latency inference, deploying the model behind an HTTPS endpoint that autoscales to handle request traffic. This directly meets the requirement for serving predictions via a REST API with minimal latency, as the endpoint keeps the model loaded and ready to respond to individual requests in milliseconds.
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 interactive development, not production serving.
- ✓
SageMaker real-time endpoint
Why this is correct
Real-time endpoints provide low-latency REST API.
- ✗
SageMaker Processing job
Why it's wrong here
Processing is for data transformation, not serving.
- ✗
SageMaker Batch Transform
Why it's wrong here
Batch Transform is for asynchronous batch predictions.
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