- A
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.
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.
- B
Use Multi-Container Endpoints to deploy multiple models on a single instance.
Why wrong: Multi-Container Endpoints run multiple containers per endpoint but all serve the same model pipeline; they are not intended for multiple distinct models.
- C
Migrate all models to AWS Lambda functions for serverless inference.
Why wrong: AWS Lambda has hard limits on deployment package size and runtime duration, making it unsuitable for large models (5 GB) and high latency requirements.
- D
Keep individual endpoints but switch to Graviton-based instances for cost savings.
Why wrong: 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.
MLA-C01 Deployment and Orchestration of ML Workflows Practice Question
This MLA-C01 practice question tests your understanding of deployment and orchestration of ml workflows. Compare every option against the stated constraints before choosing — the best answer satisfies all requirements, not just the most obvious one. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
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?
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.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
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.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
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.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates 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.
Detailed technical explanation
How to think about this question
Under the hood, MME uses a model cache on the instance's local storage (e.g., EBS or instance store) and loads models on-demand via the SageMaker InvokeEndpoint API with a TargetModel parameter, allowing each model to be served from the same endpoint. The cache eviction policy (LRU) ensures that infrequently used models are removed, which is critical for unpredictable traffic patterns. In contrast, MCE requires all containers to be pre-loaded and running, consuming memory even for idle models, which negates cost savings for variable workloads.
KKey Concepts to Remember
- Read the scenario before looking for a memorised answer.
- Find the constraint that changes the correct option.
- Eliminate answers that are true in general but not in this case.
TExam Day Tips
- Watch for words such as best, first, most likely and least administrative effort.
- Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
A startup's cloud architect reviews their monthly bill and notices costs are higher than expected for a long-running batch job. Switching from on-demand instances to Reserved Instances — or using Spot/Preemptible VMs — can reduce compute costs by up to 72 %. Questions like this test whether you understand the tradeoffs between commitment, flexibility, and cost across cloud pricing models.
What to study next
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FAQ
Questions learners often ask
What does this MLA-C01 question test?
Deployment and Orchestration of ML Workflows — This question tests Deployment and Orchestration of ML Workflows — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: 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.
What should I do if I get this MLA-C01 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 24, 2026
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