MLA-C01 Deployment and Orchestration of ML Workflows Practice Question
A team has 200 small ML models that need to be served via HTTPS endpoints. Each model is used infrequently, and the team wants to minimize hosting costs. Which SageMaker deployment approach is MOST cost-effective?
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
✓
Use a single multi-model endpoint (MME)
Multi-model endpoints (MME) allow hosting multiple models on a single endpoint, sharing instances and reducing costs, especially for infrequently used models.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Use SageMaker Serverless Inference for each model
Why it's wrong here
While serverless is cost-effective, managing 200 separate endpoints is less optimal than a single MME; also serverless may have cold start issues.
- ✗
Deploy each model on a separate real-time endpoint
Why it's wrong here
Separate endpoints for each model would incur high costs due to idle provisioned instances.
- ✗
Use Batch Transform for all models
Why it's wrong here
Batch Transform is not suitable for real-time HTTPS serving.
- ✓
Use a single multi-model endpoint (MME)
Why this is correct
MME dynamically loads models from Amazon S3 onto shared instances, minimizing cost for many infrequently used models.
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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.