Google Cloud Professional Machine Learning Engineer — Question 127

You work for a gaming company that develops massively multiplayer online (MMO) games. You built a TensorFlow model that predicts whether players will make in-app purchases of more than $10 in the next two weeks. The model’s predictions will be used to adapt each user’s game experience. User data is stored in BigQuery. How should you serve your model while optimizing cost, user experience, and ease of management?

Answer options

• A. Import the model into BigQuery ML. Make predictions using batch reading data from BigQuery, and push the data to Cloud SQL
• B. Deploy the model to Vertex AI Prediction. Make predictions using batch reading data from Cloud Bigtable, and push the data to Cloud SQL.
• C. Embed the model in the mobile application. Make predictions after every in-app purchase event is published in Pub/Sub, and push the data to Cloud SQL.
• D. Embed the model in the streaming Dataflow pipeline. Make predictions after every in-app purchase event is published in Pub/Sub, and push the data to Cloud SQL.

Correct answer: A

Explanation

The correct answer, A, is optimal because it allows for efficient batch processing directly within BigQuery ML, leveraging existing user data without incurring additional costs for separate infrastructure. The other options either involve more complexity in deployment or utilize services that may not be as cost-effective or straightforward for batch predictions as BigQuery ML.