AIF-C01 practice question: Choosing FMs and inference parameters (3 of 7)
Applications of Foundation Models A free question from our original AIF-C01 practice exam. Try it, then open the answer to see why each option is right or wrong.
A media company runs a summarization feature on Amazon Bedrock.
For three months it expects low, unpredictable traffic while it tests demand. After that it expects steady, high volume that needs reserved throughput.
Which pricing approach is MOST cost-effective in each phase?
Choose one answer.
AA. Provisioned Throughput during testing, then on-demand for the steady phase
BB. On-demand during testing, then Provisioned Throughput for the steady phase
CC. Provisioned Throughput with a six-month commitment in both phases
DD. Batch inference in both phases, since its discounted rate suits any workload
Show the answer and explanation
Answer: B
A. Wrong: This is the wrong way round. Provisioned Throughput bills a fixed hourly rate, so paying for reserved capacity while traffic is low and unpredictable wastes money. On-demand suits that phase better.
B. Right: Correct. On-demand bills per token used with no commitment, which suits uncertain early traffic. Provisioned Throughput reserves capacity at a fixed hourly cost, which suits steady high volume once demand is known. Learn more: Provisioned Throughput for Amazon Bedrock (opens in a new tab)
C. Wrong: A six-month commitment locks in capacity before demand is known, so the company could pay for unused capacity during testing. A commitment makes sense only once steady demand is proven.
D. Wrong: Batch inference runs as a job over a set of prompts at a lower price for select models. The summarization feature needs live results, so batch does not fit either phase.
Explanation
On-demand pricing charges per token with no commitment, so it suits testing and variable traffic. Provisioned Throughput buys reserved model capacity at a fixed hourly rate, with lower hourly prices for longer commitments, so it suits steady, high-volume workloads. Batch inference is cheaper but is designed for jobs rather than live features.
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