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. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. 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 deploying multiple models on a single endpoint to reduce costs. They need to update one model without affecting others. Which solution?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Use SageMaker Multi-Model Endpoint
SageMaker Multi-Model Endpoint (MME) allows hosting multiple models on a single endpoint, sharing the underlying compute instance. When you need to update one model, you can simply upload a new model artifact (e.g., a new `model.tar.gz`) to Amazon S3, and the endpoint will automatically load the updated version on subsequent inference requests without affecting the other models currently cached or in use.
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 multiple single-model endpoints behind an Application Load Balancer
Why it's wrong here
This increases cost and complexity; not the most efficient.
✗
Use SageMaker Batch Transform for some models
Why it's wrong here
Batch Transform is for batch predictions, not real-time inference.
✓
Use SageMaker Multi-Model Endpoint
Why this is correct
Multi-model endpoints host multiple models and allow updating one model independently.
Related concept
Read the scenario before looking for a memorised answer.
✗
Use a SageMaker Endpoint with multiple production variants
Why it's wrong here
Production variants are for A/B testing, not for hosting multiple models separately.
Common exam traps
Common exam trap: answer the scenario, not the keyword
AWS often tests the distinction between multi-model endpoints (for hosting many models on one endpoint) and production variants (for routing traffic between versions of the same model), leading candidates to incorrectly choose option D when they need to update one model independently.
Detailed technical explanation
How to think about this question
Under the hood, SageMaker MME uses a shared container (e.g., a multi-model server) that dynamically loads model artifacts from S3 into memory based on the `TargetModel` parameter in the invocation request. The endpoint caches models in memory (up to a configurable limit), and when a model is updated, the new artifact is fetched from S3 on the next request, while other models remain untouched. This is particularly cost-effective for scenarios like serving many lightweight models (e.g., per-customer or per-region models) where traffic is sparse or bursty, as you pay for a single instance instead of one per model.
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
Got this wrong? Here's your next step.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
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 SageMaker Multi-Model Endpoint — SageMaker Multi-Model Endpoint (MME) allows hosting multiple models on a single endpoint, sharing the underlying compute instance. When you need to update one model, you can simply upload a new model artifact (e.g., a new `model.tar.gz`) to Amazon S3, and the endpoint will automatically load the updated version on subsequent inference requests without affecting the other models currently cached or in use.
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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Question Discussion
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