AIF-C01 Fundamentals of AI and ML Practice Question
A financial services company needs to deploy a real-time fraud detection model with sub-100ms inference latency. The model is a large ensemble requiring 8 GB of memory per request. The workload has bursty traffic. Which Amazon SageMaker deployment strategy best meets these requirements?
⚠ Common exam trap
Candidates often assume multi-model endpoints (Option C) are suitable for large models, but they are designed for many small models sharing memory, not for a single large ensemble requiring 8 GB per request.
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
✓
Use a single ml.r5.2xlarge instance with an auto-scaling policy based on CPU utilization
A single ml.r5.2xlarge instance provides 16 GB of memory, which can handle the 8 GB per request requirement, and SageMaker real-time endpoints with auto-scaling based on CPU utilization can dynamically adjust to bursty traffic while maintaining sub-100ms inference latency. This approach avoids the overhead of load balancers or multi-model caching that could introduce latency.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Deploy behind an Application Load Balancer with multiple ml.m5.xlarge EC2 instances running the model
Why it's wrong here
This is not managed by SageMaker; requires custom infrastructure management and does not guarantee low latency.
- ✓
Use a single ml.r5.2xlarge instance with an auto-scaling policy based on CPU utilization
Why this is correct
A real-time endpoint with a large instance and auto-scaling handles bursty traffic and meets latency requirements.
- ✗
Use a SageMaker multi-model endpoint with ml.m5.large instances to cache multiple models
Why it's wrong here
Multi-model endpoints have model loading overhead, increasing latency beyond 100ms.
- ✗
Use SageMaker asynchronous inference with a large batch size
Why it's wrong here
Asynchronous inference is not real-time and has higher latency.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This AIF-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 AIF-C01 exam.