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Deployment and Orchestration of ML WorkflowseasyMultiple ChoiceObjective-mapped

MLA-C01 Deployment and Orchestration of ML Workflows Practice Question

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?

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

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.

Answer analysis

Option-by-option breakdown

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

  • Multi-container endpoint

    Why it's wrong here

    Multi-container endpoints run different containers per request, not multiple models of the same framework. They are for microservice architectures.

  • Batch transform job

    Why it's wrong here

    Batch transform jobs process entire datasets offline and return predictions to Amazon S3, lacking the real-time inference endpoint needed to serve all 50 models on demand from a single URL. This option is tempting because batch transforms are cost-effective for infrequent, large-scale inference on static data, and would be correct if the company could queue all requests as a single batch job rather than requiring live, per-request responses.

  • Real-time endpoint with 50 production variants

    Why it's wrong here

    Production variants are for traffic splitting across different endpoint configurations, not for hosting many models on a single instance.

  • Multi-model endpoint

    Why this is correct

    MME hosts many models on one endpoint, loading each model on demand. Ideal for many small, 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.