MLA-C02 practice question: Real-time, serverless, async and batch inference (3 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 logistics broker scores about 40 million shipments every night to predict which ones will miss their delivery window. Results are used the next morning in a planning report, and nobody needs an answer during the day.
The team wants the scores in Amazon S3, with no endpoint running between the nightly runs.
Which approach meets these requirements?
Choose one answer.
AA. A real-time endpoint that runs all day, with the scoring service calling it once for each shipment in the nightly batch.
BB. A serverless inference endpoint that the nightly job calls for each shipment, one request at a time.
CC. A multi-model endpoint that loads the shipment model from S3 when a request arrives, with the nightly job sending one request per shipment.
DD. A batch transform job that reads the shipment files from S3, scores them with the model, and writes the predictions back to S3.
Show the answer and explanation
Answer: D
A. Wrong: A running endpoint is paid for all day, and calling it once per shipment is slower than a bulk job designed for offline scoring. The stem asks for no endpoint between runs.
B. Wrong: Calling a serverless endpoint once for each of 40 million shipments is slow and expensive, and it is a request-time pattern. Offline scoring of a dataset is what batch transform is built for.
C. Wrong: A multi-model endpoint is a real-time endpoint for many models, and it runs continuously. It does not remove the need for an always-on serving layer.
Batch transform scores a large dataset offline, then releases its compute and writes results to S3. Real-time and serverless endpoints serve requests one at a time, and a multi-model endpoint still runs continuously.
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