Google Cloud Professional Data Engineer Practice Exam 2025 – Complete Preparation

BigQuery ML is designed specifically for creating and executing machine learning models using standard SQL queries. This integration allows data analysts and data scientists to build and deploy models directly within BigQuery, leveraging its powerful data processing capabilities without needing to export the data to separate machine learning tools or environments.

By utilizing SQL, users can easily formulate complex queries to prepare their data and apply machine learning algorithms directly on that data, simplifying the workflow significantly. This capability enables users to use familiar SQL syntax for both data manipulation and model building, making machine learning more accessible to those who may not have extensive programming knowledge.

The other options focus on tasks that do not align with the core purpose of BigQuery ML. Data extraction tasks and storage management are crucial aspects of data handling but are not the primary functions of BigQuery ML itself. Similarly, while visualizing data is important in the data analysis process, it is not directly related to the functionality provided by BigQuery ML for training and executing machine learning models.