MLA-C01 Deployment and Orchestration of ML Workflows Practice Question
A team deploys a model on a SageMaker real-time endpoint using an ml.m5.xlarge instance. The model has high latency due to a large neural network. The team wants to reduce latency without changing the model code. Which option should they use?
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
Amazon Elastic Inference attaches a fixed amount of GPU acceleration to an EC2 instance, providing cost-effective acceleration for deep learning inference without needing 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.
- ✗
Increase the instance size to ml.m5.4xlarge
Why it's wrong here
More CPU power may not help a neural network latency; GPU acceleration is more effective.
- ✓
Attach Amazon Elastic Inference to the endpoint
Why this is correct
Elastic Inference provides GPU acceleration at lower cost than a full GPU instance, reducing inference latency.
- ✗
Use SageMaker Neo to compile the model
Why it's wrong here
Neo optimizes for target hardware but this option is not about changing instance type; the user wants to keep the same instance.
- ✗
Switch to a GPU instance like ml.g4dn.xlarge
Why it's wrong here
Switching to GPU instance reduces latency but may be more expensive than Elastic Inference attached to the current instance.
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