Dynatrace dashboards offer extensive capabilities for filtering observability data to prioritize relevant information effectively. The tutorial discusses various filtering techniques, such as applying instant ad-hoc filters on individual tiles, using dynamic filtering across multiple tiles with variables, and leveraging Segments for platform-wide filtering aligned with business contexts. These methods enhance the efficiency of monitoring and troubleshooting, especially in complex environments like Kubernetes. Users can filter data using natural language or Dynatrace Query Language (DQL) with the help of Davis CoPilot™ for more sophisticated querying. The use of variables and Segments allows for dynamic and context-aware data visualization, making it easier to manage and analyze large datasets efficiently. This approach not only improves performance by reducing data volume but also facilitates a more intuitive and interactive dashboard experience, as demonstrated through practical examples and tutorials.