How are the system parameters proactively adjusted in Oracle Autonomous Database?

The proactive adjustment of system parameters in Oracle Autonomous Database is primarily achieved through its self-tuning capabilities. This feature allows the database to automatically optimize its performance based on workload patterns and usage. The self-tuning capabilities operate by utilizing machine learning algorithms that analyze various performance metrics and adjust parameters in real-time, ensuring efficient resource utilization and optimal database performance without requiring manual intervention.

These self-tuning functionalities enable the database to adapt dynamically to changing workloads and optimize configuration settings on the fly, enhancing reliability and responsiveness. This automated approach helps to reduce administrative overhead and minimizes the risk of human error in tuning database performance in response to evolving demands.

Other methods, such as manual interventions or using third-party tools, aren't inherent to the design of Oracle Autonomous Database's self-managing capabilities. Relying on customer feedback surveys would not be an efficient or timely method for adjusting system parameters, as it would lack the immediate responsiveness that self-tuning algorithms provide.