The answer is to use a SageMaker multi-model endpoint with multiple ml.g5.xlarge instances and auto scaling. This is correct because a multi-model endpoint allows you to host multiple model replicas across a fleet of instances, enabling horizontal scaling to distribute the 50 RPS load and keep per-instance latency under 500 ms, while using smaller, cost-effective CPU instances instead of a single expensive larger instance. On the AWS Certified AI Practitioner AIF-C01 exam, this scenario tests your understanding of scaling SageMaker endpoints for LLM latency under real-time constraints, often appearing as a trap where candidates mistakenly choose a larger instance type or a single GPU instance, forgetting that the model is too large for one GPU. The key memory tip is “horizontal over vertical for cost-effective throughput”—when a model is too big for one GPU, spread the load across multiple smaller CPU instances with auto scaling.
AIF-C01 Fundamentals of Generative AI Practice Question
This AIF-C01 practice question tests your understanding of fundamentals of generative ai. 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 financial services company is deploying a generative AI model on Amazon SageMaker for real-time fraud detection. The model, a fine-tuned Llama 2 7B, must respond to transaction requests within 500 milliseconds. The team has deployed the model using a SageMaker real-time endpoint with a single ml.g5.2xlarge instance. During load testing, the endpoint achieves an average latency of 450 ms at 10 requests per second (RPS), but the latency spikes to over 2 seconds at 20 RPS. The team needs to maintain sub-500 ms latency at up to 50 RPS. The model is too large to fit on a single GPU, so they are using CPU instances. They considered using a larger instance type but want to minimize cost. What should the team do to meet the latency requirement cost-effectively?
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 the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Use a SageMaker multi-model endpoint with multiple ml.g5.xlarge instances and auto scaling
Option C is correct because a SageMaker multi-model endpoint (MME) allows multiple model replicas to be hosted on a fleet of instances, enabling horizontal scaling to handle increased throughput. By using multiple ml.g5.xlarge instances with auto scaling, the team can distribute the 50 RPS load across several instances, keeping per-instance latency low while minimizing cost compared to a single larger instance. This approach also leverages the fact that the model is too large for a single GPU but can be efficiently served on CPU instances with proper load distribution.
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.
✗
Upgrade to a single ml.g5.4xlarge instance
Why it's wrong here
A larger instance may still not handle 50 RPS within latency, and cost is higher.
✗
Attach an Amazon Elastic Inference accelerator to the existing instance
Why it's wrong here
Elastic Inference is deprecated and not cost-effective for this scenario.
✓
Use a SageMaker multi-model endpoint with multiple ml.g5.xlarge instances and auto scaling
Why this is correct
Distributing load across smaller instances reduces cost and meets latency via scaling.
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.
✗
Use SageMaker Serverless Inference to automatically scale
Why it's wrong here
Serverless Inference can have cold start latency exceeding 500 ms.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates assume a larger single instance (Option A) is the simplest solution, but they overlook the cost-efficiency and scalability benefits of horizontal scaling with a multi-model endpoint, which is specifically designed for high-throughput, low-latency inference with models that don't fit on a single GPU.
Trap categories for this question
Scenario analysis trap
Elastic Inference is deprecated and not cost-effective for this scenario.
Detailed technical explanation
How to think about this question
SageMaker multi-model endpoints use a shared serving container that loads and unloads models dynamically from Amazon S3, but in this case, all instances serve the same model, effectively creating a load-balanced fleet. Auto scaling policies based on invocation count or CPU utilization can scale the number of instances horizontally, ensuring that each instance handles a manageable RPS (e.g., 10 RPS per instance) to maintain latency under 500 ms. This design is cost-effective because it uses smaller, cheaper instances (ml.g5.xlarge) rather than a single expensive large instance, and scales only as needed.
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.
Fundamentals of Generative AI — This question tests Fundamentals of Generative AI — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Use a SageMaker multi-model endpoint with multiple ml.g5.xlarge instances and auto scaling — Option C is correct because a SageMaker multi-model endpoint (MME) allows multiple model replicas to be hosted on a fleet of instances, enabling horizontal scaling to handle increased throughput. By using multiple ml.g5.xlarge instances with auto scaling, the team can distribute the 50 RPS load across several instances, keeping per-instance latency low while minimizing cost compared to a single larger instance. This approach also leverages the fact that the model is too large for a single GPU but can be efficiently served on CPU instances with proper load distribution.
What should I do if I get this AIF-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.
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Question Discussion
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