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MLA-C01 Practice Question: Deploying a multi-model endpoint using SageMaker…

A company is deploying a multi-model endpoint using SageMaker to serve multiple models from a single endpoint. They notice that one model consumes excessive memory and impacts others. What is the BEST practice to isolate resource usage?

⚠ Common exam trap

It's easy for candidates to assume multi-model endpoints are designed for resource isolation, but in reality they share memory and compute, so the correct answer is to use separate endpoints for strict isolation.

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 separate endpoints for each model.

Using separate endpoints for each model ensures complete resource isolation at the instance level. When one model consumes excessive memory, it cannot impact others because each model runs on its own dedicated endpoint with its own compute resources. This is the best practice for isolating resource usage in production environments where memory-intensive models are deployed.

Answer analysis

Option-by-option breakdown

For each option: why learners choose it and why it is or isn't the right answer here.

  • Configure instance type with more memory.

    Why it's wrong here

    Increasing memory still allows one model to consume all resources.

  • Use separate endpoints for each model.

    Why this is correct

    Separate endpoints provide complete isolation of compute resources.

  • Use SageMaker Model Parallelism.

    Why it's wrong here

    Model Parallelism is for training large models, not inference isolation.

  • Use multi-model endpoint with model cache size limit.

    Why it's wrong here

    Cache limit helps with loading but does not isolate memory usage.

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Written by Johnson Ajibi, MSc IT Security

Senior Network & Security Engineer · founder of Courseiva

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