Question 57 of 835
hardMultiple ChoiceObjective-mapped
MLA-C01 Practice Question: Running multiple SageMaker endpoints for…
A company is running multiple SageMaker endpoints for different models, each serving a separate business unit. The total cost is growing rapidly. The ML engineering team wants to reduce costs without sacrificing performance or isolation. They are considering either consolidating models into a Multi-Model Endpoint (MME) or onto a Multi-Container Endpoint (MCE). The models vary in size from 100 MB to 5 GB, and traffic patterns are unpredictable. Which recommendation is MOST appropriate?
⚠ Common exam trap
Watch out — candidates often confuse Multi-Model Endpoints with Multi-Container Endpoints, assuming both provide similar isolation and cost benefits, but MCE is designed for microservices-like architectures where all containers must be active, not for dynamic model loading based on traffic.
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 Multi-Model Endpoint with a single large instance type to host all models, and enable SageMaker inference pipelines if pre-processing is needed.
A Multi-Model Endpoint (MME) is the most appropriate choice because it allows hosting multiple models on a single instance while keeping them isolated in separate memory spaces, which reduces cost by sharing the underlying infrastructure. MME dynamically loads and unloads models based on traffic, making it ideal for unpredictable patterns and model sizes ranging from 100 MB to 5 GB. Inference pipelines can be added for pre-processing without breaking the multi-model architecture, preserving performance and isolation.
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 a Multi-Model Endpoint with a single large instance type to host all models, and enable SageMaker inference pipelines if pre-processing is needed.
Why this is correct
Multi-Model Endpoints load models on demand, allowing many small models to share an instance, reducing cost. They support isolation through model directories and can be combined with inference pipelines.
- ✗
Use Multi-Container Endpoints to deploy multiple models on a single instance.
Why it's wrong here
Multi-Container Endpoints run multiple containers per endpoint but all serve the same model pipeline; they are not intended for multiple distinct models.
- ✗
Migrate all models to AWS Lambda functions for serverless inference.
Why it's wrong here
AWS Lambda has hard limits on deployment package size and runtime duration, making it unsuitable for large models (5 GB) and high latency requirements.
- ✗
Keep individual endpoints but switch to Graviton-based instances for cost savings.
Why it's wrong here
Graviton instances may save cost, but consolidating models uses a shared instance, which provides greater savings. This option does not address the core request for consolidation.
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Last reviewed: Jun 24, 2026
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.
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