- A
The team is using a multi-model endpoint, which loads models on demand; loading a model into GPU memory causes latency spikes.
Multi-model endpoints load and unload models from memory, causing latency spikes when a new model is accessed.
- B
The endpoint is configured with a single production variant, causing all traffic to overload one instance.
Why wrong: A single variant would cause consistent high latency, not spikes only when a new model is loaded.
- C
The endpoint is using the wrong instance type that lacks sufficient GPU memory.
Why wrong: If the instance type were insufficient, latency would be consistently high, not just during model loading.
- D
The model is too large for the specified container memory, causing swap to disk.
Why wrong: Swap would cause sustained high latency, not just spikes during loading.
Quick Answer
The answer is that the latency spikes are caused by the multi-model endpoint’s on-demand model loading behavior. When a new model is invoked, SageMaker must download the artifacts from Amazon S3 and load them into GPU memory, a process that is inherently slow compared to cached inference, creating a cold-start latency spike. On the AWS Certified Machine Learning Engineer Associate MLA-C01 exam, this question tests your understanding of the trade-offs between multi-model endpoints and single-model endpoints, specifically how GPU memory management differs from CPU-based MMEs. A common trap is assuming the GPU instance type is underpowered or that network latency is the culprit, but the core issue is the time required to load model weights into GPU VRAM. Remember the memory tip: “MME means model memory miss equals latency spike”—if the model isn’t cached, the GPU pays the price.
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. Match the stated requirement to the specific cloud service, access model, or configuration option — many options are valid in isolation but not for this scenario. 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.
An ML team is deploying a model using SageMaker. The model requires GPU inference and must be available in multiple AWS regions for low latency. The team has created a multi-model endpoint with GPU instances. After deployment, they notice high latency spikes when a new model is loaded. What is the most likely cause?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue:
"most likely"Why it matters: Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.
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
The team is using a multi-model endpoint, which loads models on demand; loading a model into GPU memory causes latency spikes.
A multi-model endpoint (MME) loads models on demand from Amazon S3 into the instance's memory. When a new model is requested and not already cached, SageMaker must download the model artifacts and load them into GPU memory, which is a time-consuming operation that causes a latency spike for the first inference request. This cold-start behavior is inherent to MMEs and explains the observed spikes.
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.
- ✓
The team is using a multi-model endpoint, which loads models on demand; loading a model into GPU memory causes latency spikes.
Why this is correct
Multi-model endpoints load and unload models from memory, causing latency spikes when a new model is accessed.
Clue confirmation
The clue word "most likely" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
The endpoint is configured with a single production variant, causing all traffic to overload one instance.
Why it's wrong here
A single variant would cause consistent high latency, not spikes only when a new model is loaded.
- ✗
The endpoint is using the wrong instance type that lacks sufficient GPU memory.
Why it's wrong here
If the instance type were insufficient, latency would be consistently high, not just during model loading.
- ✗
The model is too large for the specified container memory, causing swap to disk.
Why it's wrong here
Swap would cause sustained high latency, not just spikes during loading.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates may confuse multi-model endpoint cold-start latency with general endpoint misconfiguration (like instance type or variant count), but the key clue is the timing of the spikes—only when a new model is loaded—which directly points to the on-demand loading behavior of MMEs.
Detailed technical explanation
How to think about this question
Under the hood, SageMaker MMEs use a model cache on the instance's local storage (e.g., NVMe SSD) and load models into GPU memory on first request. The latency spike occurs because the model must be downloaded from S3 (which can take seconds for large models) and then loaded into GPU memory, which involves allocating tensors and initializing the inference engine. In a real-world scenario, if models are frequently evicted due to cache pressure, each new model invocation will incur this cold-start penalty, making it critical to tune the model cache size or use a dedicated endpoint for latency-sensitive workloads.
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 media company stores terabytes of video archives that are accessed once a year for audit purposes. Moving these objects to a cold storage tier (Azure Archive, S3 Glacier, or Google Nearline) costs a fraction of hot storage. Questions like this test whether you understand storage tiers, access frequency tradeoffs, and retrieval latency requirements.
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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Deployment and Orchestration of ML Workflows — study guide chapter
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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: The team is using a multi-model endpoint, which loads models on demand; loading a model into GPU memory causes latency spikes. — A multi-model endpoint (MME) loads models on demand from Amazon S3 into the instance's memory. When a new model is requested and not already cached, SageMaker must download the model artifacts and load them into GPU memory, which is a time-consuming operation that causes a latency spike for the first inference request. This cold-start behavior is inherent to MMEs and explains the observed spikes.
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: "most likely". Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.
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: Jun 24, 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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