MLA-C01 Deployment and Orchestration of ML Workflows Practice Question
Which SageMaker feature compiles a trained model into an optimized binary for a specific hardware target (e.g., Intel, ARM, NVIDIA, or edge devices) to improve inference performance?
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 Neo
SageMaker Neo is a model compilation service that optimizes models for specific hardware targets. Amazon Elastic Inference attaches GPU acceleration to endpoints, but does not compile models. Model Monitor monitors quality. SageMaker Clarify explains 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 Model Monitor
Why it's wrong here
Model Monitor detects data drift and quality deviations in deployed endpoints; it never compiles or recompiles model artefacts. It is tempting because it also targets production inference, but its role is observability, not hardware-specific binary generation for Intel, ARM, NVIDIA or edge targets.
- ✓
SageMaker Neo
Why this is correct
SageMaker Neo compiles trained models into optimised executables for specific hardware targets, including Intel, ARM, NVIDIA and edge devices. This directly satisfies the stem's requirement for a hardware-specific binary that improves inference performance, unlike training or hosting features that leave the model format unchanged.
- ✗
Amazon Elastic Inference
Why it's wrong here
Elastic Inference attaches fractional GPU acceleration to existing endpoints; it does not compile a model into a target-specific binary. It is tempting because it also improves inference performance, but it addresses compute capacity rather than producing optimised artefacts for Intel, ARM, NVIDIA or edge hardware.
- ✗
SageMaker Clarify
Why it's wrong here
Clarify explains model predictions and detects bias in training data and models; it performs no compilation. It is tempting because it is a distinct SageMaker inference-phase feature, yet its purpose is explainability reporting, not generating optimised binaries for specific Intel, ARM, NVIDIA or edge targets.
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