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AIF-C01 Practice Question 41: Asynchronous Inference

AWS Certified AI Practitioner (AIF-C01) · Test 5 · question 1 of 10

Fundamentals of AI and MLmedium

A medical imaging company runs a model on Amazon SageMaker AI that analyzes high-resolution scans. Each request carries a payload of several hundred megabytes and can take up to 20 minutes to process. Requests arrive unpredictably throughout the day, results are needed within minutes rather than the next day, and the company doesn't want to pay for idle instances when no scans are waiting. Which inference option fits best?

Select one answer
Show answer and explanation

Answer:

  • C. An asynchronous inference endpoint

SageMaker Asynchronous Inference queues incoming requests and processes them asynchronously. It is ideal for requests with large payload sizes (up to 1 GB), long processing times (up to one hour), and near real-time latency requirements. The payload is placed in Amazon S3, and results are written back to S3, with optional notifications. It can also autoscale the instance count to zero when there are no requests, so the company pays only while scans are being processed (option C).

- A is wrong because real-time inference is designed for interactive, low-latency requests. It isn't suited to payloads of hundreds of megabytes that take many minutes to process, and its instances stay provisioned while idle. - B is wrong because a nightly batch job means results arrive the next day, which misses the "within minutes" requirement. - D is wrong because serverless inference suits workloads with idle periods that can tolerate cold starts, but it isn't built for very large payloads and long-running requests like these.

Reference: Asynchronous inference

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