MLA-C01 Deployment and Orchestration of ML Workflows Practice Question
A company has 200 small PyTorch models that are each used infrequently but need to be available for real-time inference. To minimize costs, they want to host all models on a single endpoint. Which SageMaker feature 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
✓
Multi-model endpoint (MME)
Multi-model endpoints allow hosting hundreds of models on a single endpoint, automatically loading/unloading models based on traffic. Multi-container endpoints are for different containers, not multiple models. Batch and asynchronous are not real-time.
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Variation 1. A company has 50 small PyTorch models that are used infrequently for inference. They want to minimize costs while maintaining the ability to serve all models from a single endpoint. Which SageMaker feature should they use?
easy- A.Multi-container endpoint
- B.Batch transform job
- C.Real-time endpoint with 50 production variants
- ✓ D.Multi-model endpoint
Why D: Multi-model endpoints (MME) allow hosting multiple models on a single endpoint, loading models dynamically based on the target model in the request. This reduces cost for many small, infrequently used models by sharing the underlying instance.
JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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.