Google Professional Machine Learning Engineer - 340 Questions

1. A data analyst at a retail company wants to predict future sales using historical transaction data stored in BigQuery. They need a model that handles seasonality and trend decomposition without writing custom ML code. Which BigQuery ML model type should they use?

2. A machine learning engineer needs to build a customer churn prediction model using data already in BigQuery. The target variable is binary (churned or not churned). Which BigQuery ML model type is most appropriate?

3. A machine learning engineer has trained a BigQuery ML model and wants to evaluate its performance using metrics such as precision, recall, and F1 score. Which BigQuery ML function should they use?

4. A company wants to build a product recommendation engine for their e-commerce platform. The data consists of user-item interaction ratings stored in BigQuery. They want to use a collaborative filtering approach without writing custom code. Which BigQuery ML model type should they use?

5. A machine learning engineer needs to generate text embeddings from customer reviews stored in BigQuery to use in a downstream similarity search. They want to use a foundation model without leaving the BigQuery environment. Which approach should they use?