- A
SageMaker real-time endpoint with Multi-Model Endpoint (MME) on an ml.g4dn instance
MME on GPU instances allows multiple models to share the same GPU, reducing cost while meeting latency requirements.
- B
SageMaker real-time endpoint with a single model per ml.g4dn instance
Why wrong: This would not share the instance across multiple models, leading to higher cost for hosting many models.
- C
SageMaker Serverless Inference
Why wrong: Serverless does not support GPU instances, so it cannot provide GPU acceleration.
- D
SageMaker Asynchronous Inference
Why wrong: Asynchronous inference is designed for large payloads and does not guarantee sub-100ms latency.
Question 274 of 1,000
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 data science team needs to deploy a PyTorch model that performs real-time inference with sub-100ms latency. The model requires GPU acceleration, but the team wants to minimize cost by sharing GPU instances across multiple models. Which SageMaker hosting option 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.
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
✓
SageMaker real-time endpoint with Multi-Model Endpoint (MME) on an ml.g4dn instance
Option A is correct because SageMaker Multi-Model Endpoint (MME) allows multiple PyTorch models to share a single GPU instance (e.g., ml.g4dn), reducing cost while meeting sub-100ms latency requirements. MME dynamically loads and unloads models into GPU memory based on traffic, enabling real-time inference with GPU acceleration without dedicating a full instance per model.
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.
- ✓
SageMaker real-time endpoint with Multi-Model Endpoint (MME) on an ml.g4dn instance
Why this is correct
MME on GPU instances allows multiple models to share the same GPU, reducing cost while meeting latency requirements.
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.
- ✗
SageMaker real-time endpoint with a single model per ml.g4dn instance
Why it's wrong here
This would not share the instance across multiple models, leading to higher cost for hosting many models.
- ✗
SageMaker Serverless Inference
Why it's wrong here
Serverless does not support GPU instances, so it cannot provide GPU acceleration.
- ✗
SageMaker Asynchronous Inference
Why it's wrong here
Asynchronous inference is designed for large payloads and does not guarantee sub-100ms latency.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often confuse SageMaker Serverless Inference with GPU support, but Serverless does not provide GPU acceleration, making it unsuitable for this latency-sensitive GPU workload.
Detailed technical explanation
How to think about this question
SageMaker MME on GPU instances uses NVIDIA Triton Inference Server under the hood to manage model loading and inference across multiple models in shared GPU memory. The key subtlety is that MME supports GPU-accelerated models only with Triton (not the default TorchServe), and models must be packaged in a specific format (e.g., TorchScript or ONNX) to enable dynamic batching and efficient memory sharing. In a real-world scenario, a team serving multiple PyTorch NLP models can reduce costs by up to 90% compared to single-model endpoints, but must monitor GPU memory fragmentation to avoid out-of-memory errors during model swapping.
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.
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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: SageMaker real-time endpoint with Multi-Model Endpoint (MME) on an ml.g4dn instance — Option A is correct because SageMaker Multi-Model Endpoint (MME) allows multiple PyTorch models to share a single GPU instance (e.g., ml.g4dn), reducing cost while meeting sub-100ms latency requirements. MME dynamically loads and unloads models into GPU memory based on traffic, enabling real-time inference with GPU acceleration without dedicating a full instance per model.
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.
Are there clue words in this question I should notice?
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.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
About these practice questions
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Last reviewed: Jul 4, 2026
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.
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