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SAP-C03 practice question: AI workload costs (Bedrock tiers, batch inference, caching, cross-Region inference) (1 of 2)

Cost-Optimized Architecture Design A free question from our original SAP-C03 practice exam. Try it, then open the answer to see why each option is right or wrong.

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A news publisher uses an Amazon Bedrock model to write summaries for about two million articles each night. The summaries are read the next morning, so no request needs an answer in seconds.

The article texts for each night are written to Amazon S3 by 11 PM, and every summary must be ready by 6 AM. The publisher's finance team wants the lowest cost per summary, and it does not want a fixed-capacity commitment.

Which solution meets these requirements?

Choose one answer.

  1. A. Call the model on demand for each article during the night with the default settings, so that each request is answered as fast as possible at the standard on-demand rate.
  2. B. Route each request through a cross-Region inference profile, so that requests are spread across Regions and each one finishes sooner.
  3. C. Buy a one-month provisioned throughput commitment for the model and send the nightly summaries through it, so every request gets dedicated capacity.
  4. D. Write the night's requests as JSONL files in S3, run them as an Amazon Bedrock batch inference job, and load the summaries from the job's S3 output before 6 AM.
Show the answer and explanation

Answer: D

  • A. Wrong: On-demand calls work, but they are billed at the standard rate even though no request needs a fast answer. Batch inference is priced lower for this kind of offline job.
  • B. Wrong: Cross-Region inference helps with throughput and availability, but it does not lower the per-token cost of the summaries. The publisher's cost goal is not met by this change.
  • C. Wrong: Provisioned throughput charges for reserved capacity whether or not the job uses it. The nightly pattern does not need dedicated capacity, and the finance team asked to avoid a fixed commitment.
  • D. Right: Correct. Batch inference processes large offline sets of prompts from S3 at a lower price than on-demand calls, and it needs no capacity commitment. The overnight window gives the job time to finish before the summaries are needed.

Explanation

Batch inference suits an offline nightly job whose input is already in S3, and it costs less than on-demand calls without any commitment. On-demand calls cost more, provisioned throughput is a commitment, and cross-Region routing does not reduce the per-token cost.

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