AIP-C01 practice question: Choosing how to deploy models (on-demand, Provisioned Throughput, SageMaker AI endpoints)
Implementation and Integration A free question from our original AIP-C01 practice exam. Try it, then open the answer to see why each option is right or wrong.
A recruiting platform is adding a feature that summarizes candidate resumes. The feature is new, so the team has no traffic history yet. Usage is spiky: recruiters upload batches on weekday mornings, and on most other days the feature handles only a few hundred summaries.
The team wants to pay only for the tokens it actually processes, and it does not want to commit to hourly capacity until real usage patterns are known. During a batch, each summary must return within a few seconds.
Which deployment option is the MOST cost-effective for these requirements?
Choose one answer.
AA. Call the foundation model on demand through the Amazon Bedrock Converse API, paying per token processed, and retry throttled requests with exponential backoff.
BB. Purchase Provisioned Throughput with a one-month commitment, sized for the weekday morning peak, so that every request has dedicated capacity.
CC. Deploy the model to a SageMaker AI real-time endpoint on GPU instances that run around the clock, with autoscaling to a minimum of one instance.
DD. Run the summaries with a SageMaker AI batch transform job each night over that day's uploads, so that each resume is processed at the lowest cost per job.
Show the answer and explanation
Answer: A
A. Right: On-demand pricing charges only for tokens processed, with no commitment and no idle capacity. Short responses during a batch meet the latency target, and the team can move to reserved capacity later if usage grows.
B. Wrong: Provisioned Throughput bills for reserved model units every hour, including the many days with little traffic. This conflicts with the pay-only-for-tokens requirement before any usage pattern is known.
C. Wrong: A real-time endpoint keeps GPU instances running at night and on quiet days, so the team pays for idle hours. It also adds endpoint and model operations the team does not need for a new feature.
D. Wrong: Batch transform is cheap per job, but results arrive hours later, which misses the requirement of a few seconds per summary during a batch. Recruiters need answers while they are working.
Explanation
On-demand inference suits a new feature with unknown and spiky traffic, because the team pays only for tokens processed and has no hourly commitment. Provisioned Throughput and always-on endpoints charge for capacity during quiet periods, and batch transform gives up the seconds-level response that recruiters need.
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