Which two workload types can be chosen when creating an autonomous database?

When creating an autonomous database, selecting the right workload type is essential for optimizing performance based on the intended use case. The correct answer identifies "Data Warehouse" as one of the workload types, which is designed to manage and analyze large volumes of data efficiently. This configuration is optimized for analytical queries, reporting, and business intelligence workloads, allowing for complex queries that often involve aggregating large datasets.

The ability to specify a "Data Warehouse" workload type ensures that the database is automatically optimized for these scenarios, including features like parallel query execution, advanced compression, and data partitioning, which are fundamental for effective data analysis. This focus on query performance and efficiency makes it a preferable choice for users who need to extract insights from large amounts of data.

The other options highlight different contexts or types of database architectures. While "Transaction Processing" is also a valid workload type, making it a complementary but distinct choice, "OLAP" (Online Analytical Processing) typically refers to specific methodologies and is not categorized as a workload type in the context of creating an autonomous database. The "Database" option is too broad and non-specific, lacking the context necessary to qualify it as a workload type in this scenario. Therefore, the most appropriate choices for defining workload types in the