MLA-C01 Deployment and Orchestration of ML Workflows Practice Question

A company uses SageMaker Neo to compile a trained model for deployment on edge devices. What is the primary benefit of using Neo?

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

✓

It reduces model size and improves inference speed on target hardware

SageMaker Neo optimizes models for specific hardware architectures (e.g., ARM, Intel, NVIDIA) to achieve faster inference and lower memory footprint.

Answer analysis

For each option: why learners choose it and why it is or isn't the right answer here.

✗
It monitors model drift in production
Why it's wrong here
Model monitoring is separate (SageMaker Model Monitor).
✓
It reduces model size and improves inference speed on target hardware
Why this is correct
Neo uses hardware-specific optimizations like kernel fusion and quantization to improve performance.
✗
It automatically retrains the model on new data
Why it's wrong here
Neo compiles models, does not retrain.
✗
It provides a serverless inference endpoint
Why it's wrong here
Neo does not serve endpoints; it compiles models.

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