MLA-C02 practice question: Multi-model and multi-container endpoints
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 dental chain has trained a separate small demand-forecasting model for each of its 300 clinics. Each model is only a few megabytes, and most clinics receive a prediction request only a few times a day.
The team wants all the models served from one SageMaker AI endpoint to keep hosting costs down, and it accepts that the first request for a rarely used clinic may load its model more slowly.
Which hosting approach meets these requirements?
Choose one answer.
AA. Deploy one multi-model endpoint, store the 300 model artifacts under one S3 prefix, and specify the model to invoke in each request.
BB. Deploy 300 separate real-time endpoints, one for each clinic model, so that each clinic has its own capacity.
CC. Combine all 300 models into one large model file that is loaded into memory when the endpoint starts, and route requests inside the model code.
DD. Run a batch transform job every hour over all 300 clinics, and let the front-end read the results from S3.
Show the answer and explanation
Answer: A
A. Right: Correct. A multi-model endpoint hosts many models behind one endpoint and loads each model from S3 when it is first requested. This suits many small, infrequently used models and accepts a slower first request. Learn more: Multi-model endpoints (opens in a new tab)
B. Wrong: Three hundred endpoints multiply the hosting cost and management effort for models that receive few requests. The stem asks for one endpoint to keep costs down.
C. Wrong: A combined model changes how the forecasts are produced and mixes unrelated clinics, which the team does not want. The option also loads everything at start-up rather than on demand.
D. Wrong: Batch transform is offline and not built to serve a request for one clinic at a time. The stem describes on-demand prediction requests.
Explanation
A multi-model endpoint hosts many models behind one endpoint, loading each model from S3 on first use. It suits many small, infrequently used models. Separate endpoints multiply cost, a combined model changes the forecasts, and batch transform is not an on-demand service.
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