Google Sample Question 15 of 15

Your company manages an ecommerce website. You developed an ML model that recommends additional products to users in near real time based on items currently in the user’s cart. The workflow will include the following processes: 1. The website will send a Pub/Sub message with the relevant data, and then receive a message with the prediction from Pub/Sub. 2. Predictions will be stored in BigQuery. 3. The model will be stored in a Cloud Storage bucket and will be updated frequently. You want to minimize prediction latency and the effort required to update the model. How should you reconfigure the architecture?

Source: Google Cloud OFFICIAL

Official sample question published by Google Cloud. WiseOwlLearns is not affiliated with Google LLC.

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A Write a Cloud Run function that loads the model into memory for prediction. Configure the function to be triggered when messages are sent to Pub/Sub.
B Expose the model as a Vertex AI endpoint. Write a custom DoFn in a Dataflow job that calls the endpoint for prediction.
C Use the RunInference API with WatchFilePattern in a Dataflow job that wraps around the model and serves predictions. ✓ Correct
D Create a pipeline in Vertex AI Pipelines that performs preprocessing, prediction, and postprocessing. Configure the pipeline to be triggered by a Cloud Run function when messages are sent to Pub/Sub.
🦉 Explanation by WiseOwl Tutor™ — not endorsed by Google

The RunInference API with a locally loaded model minimizes the prediction latency and makes model updates seamless by watching for new files using WatchFilePattern. Cloud Functions will run into limitations based on request rate and model size. Exposing the model as a Vertex AI endpoint and calling it from Dataflow adds to the total latency. Provisioning Vertex AI Pipelines is slow and adds significant latency, making it unsuitable for near-real-time cart predictions.

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