MLA-C02 practice question: Real-time, serverless, async and batch inference (1 of 3)
Deployment and Orchestration of ML and AI Workflows A free question from our original MLA-C02 practice exam. Try it, then open the answer to see why each option is right or wrong.
A pet insurer lets customers upload short videos of an injury when filing a claim. Each video is up to a few hundred megabytes and takes three to five minutes for the vision model to score, and customers do not need the result in the same request.
The model should scale to zero when no claims are pending, so that the insurer does not pay for idle instances, and the customer app should receive a job status to poll.
Which SageMaker AI inference option meets these requirements?
Choose one answer.
AA. A real-time endpoint with a fixed number of GPU instances, sized for the largest video, with the app waiting on the open HTTP connection for each result.
BB. An asynchronous inference endpoint, which queues large requests, scales to zero when idle, and writes results to S3 for the app to poll.
CC. A serverless inference endpoint, which lets the app send each video directly and wait for the scored result in the response.
DD. A batch transform job started once an hour over all videos uploaded in that hour, with the app reading results after each job finishes.
Show the answer and explanation
Answer: B
A. Wrong: A real-time endpoint keeps instances running when no claims are pending, and it holds the connection open for minutes, which is not reliable. The stem asks for scale to zero and a status to poll.
C. Wrong: Serverless endpoints suit smaller payloads and intermittent traffic, but this workload needs large payloads and multi-minute processing with polling. The option does not provide the queued, pollable pattern.
D. Wrong: Batch transform is designed for offline scoring of a dataset, and an hourly job adds delay to each claim. It does not give each customer a status they can poll.
Explanation
Asynchronous inference handles large payloads and long processing by queuing requests, writing results to S3, and it can scale to zero when idle. Real-time endpoints stay running and hold connections open, serverless endpoints target smaller payloads, and batch transform runs offline rather than per claim.
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