AIF-C01 Fundamentals of AI and ML Practice Question
A company wants to deploy a real-time inference endpoint for a custom model on SageMaker. The model has high latency (100ms) and they need to handle variable traffic with spikes. Which deployment strategy is most cost-effective?
⚠ Common exam trap
Test-takers frequently confuse multi-model endpoints with cost-effective scaling for a single model, not realizing that multi-model endpoints are designed for hosting many models, not for handling variable traffic for one model with high latency.
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
✓
Deploy on a single SageMaker endpoint with automatic scaling
A single SageMaker endpoint with automatic scaling allows the endpoint to dynamically adjust the number of instances based on traffic patterns, handling variable traffic and spikes cost-effectively. For a model with 100ms latency, automatic scaling can add instances during spikes and remove them during low traffic, ensuring you only pay for the compute resources you use while maintaining low inference 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 on a SageMaker multi-model endpoint
Why it's wrong here
Multi-model endpoint is designed for multiple models; for a single model, it adds unnecessary complexity.
- ✗
Use batch transform
Why it's wrong here
Batch transform is for asynchronous, not real-time.
- ✓
Deploy on a single SageMaker endpoint with automatic scaling
Why this is correct
Automatic scaling adds instances based on load, providing cost-effective handling of variable traffic.
- ✗
Use a single large instance type
Why it's wrong here
Overprovisioning leads to high cost when traffic is low.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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