MLA-C01 Deployment and Orchestration of ML Workflows Practice Question
A machine learning engineer is deploying a TensorFlow model for real-time inference. The model has high latency on CPU. Which TWO actions can reduce inference latency? (Choose two.)
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
✓
Attach Amazon Elastic Inference to the endpoint
Compiling with SageMaker Neo optimizes the model for the target hardware. Attaching Elastic Inference provides GPU acceleration without moving to a full GPU instance.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Enable SageMaker Model Monitor
Why it's wrong here
Model Monitor tracks data quality, not performance.
- ✗
Switch to a multi-model endpoint
Why it's wrong here
Multi-model endpoints reduce cost when hosting many models, but do not reduce latency for a single model.
- ✓
Attach Amazon Elastic Inference to the endpoint
Why this is correct
Elastic Inference adds GPU acceleration, reducing latency.
- ✗
Use a larger instance type with more vCPUs
Why it's wrong here
More vCPUs may not help neural network inference; GPU acceleration is more effective.
- ✓
Compile the model with SageMaker Neo
Why this is correct
Neo optimizes the model graph for faster execution.
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This MLA-C01 practice question is part of Courseiva's free Amazon Web Services certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the MLA-C01 exam.