Google Cloud Digital Leader Practice Exam

BigQuery is the correct choice for analyzing large datasets because it is a fully-managed, serverless data warehouse designed specifically for large-scale data analysis. It enables users to run SQL queries over vast amounts of data in a highly efficient manner, leveraging Google's infrastructure to perform operations quickly and cost-effectively.

BigQuery is built to handle petabytes of data, making it suitable for organizations that need to analyze large datasets without the complexities of managing the underlying infrastructure. Its capabilities include support for machine learning, geospatial analysis, and integration with other Google Cloud services, enhancing its usability for comprehensive data analysis.

The other options, while they serve important roles in data processing and storage, do not specialize in large-scale data analysis in the same way. Dataflow is primarily used for stream and batch processing, allowing for the transformation and movement of data rather than direct analysis. App Engine is a platform for building and hosting applications, and it is not specifically focused on data handling or analytics. Cloud Storage is a scalable object storage service, which is excellent for holding large volumes of data but does not provide the analytical capabilities intrinsic to BigQuery.