MLA-C01 Deployment and Orchestration of ML Workflows Practice Question
A machine learning team has a model that needs to serve predictions with very low latency (under 10 ms) for a real-time web application. The model is a small ensemble of three neural networks that fits in memory. Which SageMaker inference option is MOST appropriate?
⚠ Common exam trap
Many candidates confuse 'low latency' with 'serverless' or 'asynchronous' options, not realizing that serverless inference has cold starts and asynchronous inference adds queueing delays, both of which break the sub-10 ms requirement.
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, synchronous inference, making them the best fit for a model that must serve predictions in under 10 ms. Since the ensemble of three neural networks fits in memory, a real-time endpoint can keep the model loaded and respond to each request with minimal overhead, typically using HTTPS and the SageMaker InvokeEndpoint API.
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 batch transform
Why it's wrong here
Batch transform is for offline processing of large datasets, not real-time serving.
- ✓
SageMaker real-time endpoint
Why this is correct
Real-time endpoints are always running and can achieve sub-10 ms latency with appropriately sized instances.
- ✗
SageMaker asynchronous inference
Why it's wrong here
Asynchronous inference is designed for large payloads and longer processing times, not sub-10 ms latency.
- ✗
SageMaker serverless inference
Why it's wrong here
Serverless inference has a cold start latency that can be several seconds, which would exceed the 10 ms requirement.
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