Google Cloud Professional Data Engineer Exam 2025 – 400 Free Practice Questions to Pass the Exam

A schema in BigQuery serves as a blueprint for the dataset by defining the structure of the data. This includes specifying the column names, data types (such as STRING, INT64, FLOAT64, etc.), and relationships between the columns. By establishing these parameters, the schema ensures that the data adheres to a consistent format, facilitating efficient querying and analysis.

For instance, when you query a table, the schema allows BigQuery to correctly interpret the data types of the columns, apply appropriate operations, and optimize the query performance. Clearly defined schemas also help prevent data quality issues by enforcing structure and constraints on the data being ingested into the system, therefore contributing to better data governance and integrity.

Other options focus on aspects that are either not directly related to the fundamental purpose of a schema (data storage format, security measures, backup and recovery) or do not capture the comprehensive role of schemas in organizing and defining the details of datasets.