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 a large number of small models (each < 100 MB) for different customers. They want to minimize costs and management overhead while serving traffic that varies significantly. Which SageMaker endpoint type should they choose?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue: "minimum / minimize"
Why it matters: Asks for the least resource use — fewest addresses, smallest subnet, lowest overhead. Eliminate over-provisioned options even if they would technically work.
A batch transform job
Why wrong: Batch is not real-time.
A multi-model endpoint on a GPU instance
MME allows hosting many models on one instance, reducing costs.
A multi-variant endpoint to route traffic to different model versions
Why wrong: Multi-variant is for A/B testing, not cost sharing across many models.
A serverless endpoint
Why wrong: Serverless may be cheaper but has limitations on model size and cold starts.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
A multi-model endpoint on a GPU instance
A multi-model endpoint (MME) on a GPU instance is the best choice because it allows you to host multiple small models (< 100 MB each) on a single endpoint, sharing the underlying GPU instance to reduce costs. SageMaker MME dynamically loads and unloads models based on traffic, which minimizes management overhead and handles variable traffic patterns efficiently without provisioning separate endpoints per model.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
For each option: why learners choose it and why it is or isn't the right answer here.
A batch transform job
Why it's wrong here
Batch is not real-time.
A multi-model endpoint on a GPU instance
Why this is correct
MME allows hosting many models on one instance, reducing costs.
Clue confirmation
The clue word "minimum / minimize" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
A multi-variant endpoint to route traffic to different model versions
Why it's wrong here
Multi-variant is for A/B testing, not cost sharing across many models.
A serverless endpoint
Why it's wrong here
Serverless may be cheaper but has limitations on model size and cold starts.
Common exam traps
The trap here is that candidates confuse 'multi-model endpoint' (hosting many models on one endpoint) with 'multi-variant endpoint' (routing traffic to different versions of the same model), leading them to select option C incorrectly.
Detailed technical explanation
Under the hood, SageMaker Multi-Model Endpoints use a shared serving container that loads model artifacts from Amazon S3 on demand, caching them in memory or on disk. For GPU instances, the endpoint manages GPU memory allocation across models, which is critical because small models may not fully utilize a GPU, and MME allows you to amortize the cost across many customers. A real-world scenario is a SaaS provider serving per-customer recommendation models where traffic spikes are handled by the endpoint's auto-scaling policy, while the MME's model caching reduces cold-start latency.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
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.
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FAQ
Deployment and Orchestration of ML Workflows — This question tests Deployment and Orchestration of ML Workflows — Read the scenario before looking for a memorised answer..
The correct answer is: A multi-model endpoint on a GPU instance — A multi-model endpoint (MME) on a GPU instance is the best choice because it allows you to host multiple small models (< 100 MB each) on a single endpoint, sharing the underlying GPU instance to reduce costs. SageMaker MME dynamically loads and unloads models based on traffic, which minimizes management overhead and handles variable traffic patterns efficiently without provisioning separate endpoints per model.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Yes — watch for: "minimum / minimize". Asks for the least resource use — fewest addresses, smallest subnet, lowest overhead. Eliminate over-provisioned options even if they would technically work.
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 24, 2026
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