MLA-C01 Deployment and Orchestration of ML Workflows Practice Question
A team wants to deploy a single SageMaker real-time endpoint that serves both a PyTorch model for NLP and a TensorFlow model for image classification. Each model requires a different inference container. Which two features can they use together to achieve this? (Select 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
✓
Multi-container endpoint
A multi-container endpoint allows running multiple containers (e.g., PyTorch and TensorFlow) on the same endpoint. With inference components, each container can be associated with a specific model, and the routing logic directs requests to the appropriate container based on the model name.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Multi-model endpoint
Why it's wrong here
MME hosts multiple models but typically within a single container (same framework).
- ✓
Multi-container endpoint
Why this is correct
Multi-container endpoints can run different containers for different models.
- ✗
Production variants
Why it's wrong here
Production variants are for traffic splitting across different endpoint configurations, not for routing to different containers within the same endpoint.
- ✓
SageMaker inference components
Why this is correct
Inference components allow associating each container with a specific model and routing requests accordingly.
- ✗
SageMaker Neo compilation
Why it's wrong here
Neo is for model optimization, not for serving multiple containers.
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