AIF-C01 Applications of Foundation Models Practice Question
A startup uses Amazon Bedrock with a provisioned throughput to generate product images. They now have unpredictable traffic and want to reduce costs. What should they do?
⚠ Common exam trap
Many exam-takers assume provisioned throughput is always more cost-effective for any workload, overlooking that on-demand mode is specifically designed to eliminate idle costs for unpredictable traffic patterns.
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
✓
Switch to on-demand mode in Amazon Bedrock.
On-demand mode in Amazon Bedrock allows you to pay per inference request without committing to a provisioned throughput, making it ideal for unpredictable traffic patterns. This eliminates the cost of idle capacity while still providing access to the same foundation models. Option D directly addresses the need to reduce costs when traffic is variable.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Switch to batch inference using Amazon Bedrock.
Why it's wrong here
Batch inference suits asynchronous, delay-tolerant bulk jobs, not interactive image generation with unpredictable arrival times. It is tempting as a cost lever, but it removes real-time responsiveness, whereas on-demand inference bills per request and absorbs variable traffic without idle provisioned capacity.
- ✗
Keep the provisioned throughput but reduce the number of units.
Why it's wrong here
Provisioned throughput charges for reserved model units regardless of use, so cutting units still bills for committed capacity during quiet periods. It is tempting because it trims spend, but unpredictable demand means the reduced units either sit idle or get exceeded, whereas on-demand inference scales per request.
- ✗
Use a different model or service like Amazon SageMaker with spot instances.
Why it's wrong here
Migrating to SageMaker spot instances introduces infrastructure management and interruption risk, and spot capacity can be reclaimed mid-job. It is tempting when compute cost dominates, but the scenario needs managed, variable-throughput inference; Bedrock on-demand mode bills per request and scales automatically without re-platforming.
- ✓
Switch to on-demand mode in Amazon Bedrock.
Why this is correct
On-demand mode bills per token or image, so cost scales directly with actual usage rather than a fixed provisioned commitment. This satisfies the stem's unpredictable traffic and cost-reduction constraint, since idle provisioned capacity would otherwise be paid for regardless of demand.
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