MLS-C01 Modeling Practice Question
A company wants to deploy a machine learning model that requires very low latency predictions (under 10ms). The model is a small ensemble of decision trees. Which SageMaker deployment option is most suitable?
⚠ Common exam trap
Candidates often confuse batch processing (Batch Transform) with real-time inference, or assume that serverless options like Lambda are always the fastest, ignoring cold start and timeout constraints.
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 endpoint with a single instance
C is correct because a SageMaker endpoint with a single instance provides a persistent, real-time inference API that can achieve sub-10ms latency for a small ensemble of decision trees. The endpoint keeps the model loaded in memory and uses synchronous HTTP requests, minimizing cold start and network overhead, which is essential for low-latency predictions.
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
For development, not production hosting.
- ✗
AWS Lambda function with the model packaged
Why it's wrong here
Cold starts and not optimized for low latency.
- ✓
SageMaker endpoint with a single instance
Why this is correct
Provides real-time low-latency inference.
- ✗
SageMaker Batch Transform
Why it's wrong here
For offline predictions, not low latency.
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Senior Network & Security Engineer · founder of Courseiva
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