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 endpoints load models on demand from S3 and unload idle ones, adding cold-start latency and suiting many low-traffic models sharing an instance, not one latency-sensitive model absorbing spikes. It is tempting because it cuts hosting cost, and it would be correct when hosting hundreds of infrequently invoked models.
- ✗
Use batch transform
Why it's wrong here
Batch transform processes a complete dataset as an asynchronous job and has no persistent endpoint, so it cannot serve real-time requests or absorb traffic spikes. It is tempting because it is cheap for bulk scoring, and it would be correct for offline inference over stored data rather than interactive low-latency predictions.
- ✓
Deploy on a single SageMaker endpoint with automatic scaling
Why this is correct
Automatic scaling adjusts instance count to match traffic spikes while a single endpoint avoids paying for idle capacity during troughs. This satisfies the variable-traffic constraint cost-effectively, unlike always-on multi-instance deployments that over-provision for peak load.
- ✗
Use a single large instance type
Why it's wrong here
A single large instance provisions fixed capacity sized for peak load, so it sits idle between spikes and cannot scale out when traffic surges. It is tempting because one instance is simple to configure, and it would be correct for steady, predictable workloads where autoscaling adds no benefit.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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