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AIF-C01 practice question: Choosing FMs and inference parameters (1 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.

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An insurance company checks claim packets for fraud.

Each packet is a PDF up to 500 MB and takes up to 40 minutes to process. Packets arrive only a few times a day with long quiet gaps, and results are needed within two hours of upload. The team does not want to pay for idle servers or manage scaling.

Which SageMaker AI inference option fits BEST?

Choose one answer.

  1. A. A real-time endpoint on a fixed instance, which keeps the model loaded and answers requests with low latency.
  2. B. An asynchronous endpoint, which queues uploads, accepts up to 1 GB, runs up to an hour and scales to zero.
  3. C. A serverless endpoint, which scales to zero when idle but limits each request payload to 4 MB and each inference call to one minute.
  4. D. A batch transform job that runs once each night over the day's uploads and writes the results to Amazon S3.
Show the answer and explanation

Answer: B

  • A. Wrong: A real-time endpoint is persistent, so its instance is billed between uploads, which conflicts with the no-idle-cost rule. It is built for low-latency and sustained traffic, not large, slow files that arrive a few times a day.
  • B. Right: Correct. Asynchronous inference is built for large payloads, long processing times and queued requests. It can scale to zero when there are no requests, so the team pays nothing while idle and still gets results inside two hours. Learn more: Asynchronous inference in SageMaker AI (opens in a new tab)
  • C. Wrong: Serverless inference scales to zero, so it avoids idle cost. Its 4 MB payload limit and one-minute inference timeout cannot handle 500 MB packets that take 40 minutes. Learn more: Serverless inference in SageMaker AI (opens in a new tab)
  • D. Wrong: Batch transform suits offline jobs over data that is available up front, and it does not need a persistent endpoint. A nightly run can finish up to a day after upload, which misses the two-hour result window.

Explanation

The key constraints are large payloads, long processing, sporadic arrivals and a two-hour result window. Asynchronous inference handles payloads up to 1 GB and processing up to an hour, and it scales to zero, so it fits all of them. Serverless inference fails on payload size and timeout, and a nightly batch misses the deadline. This question tests comparing SageMaker AI inference options against specific limits.

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