C. BigQuery is the optimal choice for this scenario. It's a fully managed, serverless data warehouse that excels in analyzing large datasets with SQL queries. Its native support for prediction and geospatial processing makes it ideal for training models on vast datasets to forecast delivery delays across different regions. BigQuery efficiently handles massive data volumes and is tailored for analytical tasks involving varied data attributes.

Why not the others?

• A. Cloud Bigtable: A NoSQL database designed for large-scale operational and analytical workloads. While it's great for quick data storage and retrieval, it lacks built-in features for prediction or geospatial analysis, making it less suitable for this predictive modeling task.
• B. Cloud SQL for PostgreSQL: A managed relational database service. It's perfect for transactional workloads but not optimized for big data analytics or machine learning, lacking the necessary geospatial and predictive capabilities.
• D. Cloud Datastore: A NoSQL document database aimed at web and mobile applications. It's not designed for advanced analytics or machine learning, missing native support for geospatial processing and predictive modeling.