AIF-C01 Fundamentals of AI and ML Practice Question

A company is deploying a machine learning model for real-time fraud detection. The model must have latency under 100ms. Which infrastructure choice is most appropriate?

⚠ Common exam trap

Test-takers frequently confuse batch transform with real-time inference, assuming that any SageMaker inference capability can serve low-latency requests, but batch transform is explicitly asynchronous and designed for high-throughput, not low-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

✓

Amazon SageMaker real-time endpoints

Amazon SageMaker real-time endpoints are designed for low-latency inference, typically in the tens of milliseconds, making them suitable for real-time fraud detection where latency must be under 100ms. They deploy a model behind a persistent HTTPS endpoint that auto-scales to handle incoming requests with minimal delay.

Answer analysis

Option-by-option breakdown

For each option: why learners choose it and why it is or isn't the right answer here.

✓
Amazon SageMaker real-time endpoints
Why this is correct
Real-time endpoints provide low-latency inference with automatic scaling.
✗
Amazon EC2 with Deep Learning AMI
Why it's wrong here
While possible, this requires more operational overhead and does not guarantee sub-100ms latency without careful tuning.
✗
Amazon SageMaker batch transform
Why it's wrong here
Batch transform processes data in batches, not suitable for real-time.
✗
Amazon SageMaker notebook instance
Why it's wrong here
Notebook instances are for development and exploration, not production inference.

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AIF-C01 Fundamentals of AI and ML Practice Question

Why each option matters

Option-by-option breakdown

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