Cisco 500-420 Practice Test Questions Answers

When an Information Point is created, AppDynamics automatically collects three core method-level KPIs: Response Time, Errors per Minute, and Calls per Minute.

Response Time measures how long the configured class and method take to execute. This enables the Performance Analyst to identify degradation in a critical internal operation and compare current behavior with historical trends or baselines.

Errors per Minute records the rate at which executions of the Information Point encounter errors. This provides direct visibility into failures associated with the monitored method, even when that method is not itself a Business Transaction entry point.

Calls per Minute measures invocation frequency. It is useful for tracking operational activity such as cache access, pricing calculations, order processing, or calls to a business-critical internal service.

Minimum Response Time, Maximum Response Time, and CPU Time are not the three standard KPIs automatically generated for an Information Point in this context. Additional business values can be collected separately by configuring custom data or custom metrics.

Relevant CAAPA Study Guide topics include Information Points, Method-Level Monitoring, Calls per Minute, Response Time, Errors per Minute, Custom Data, Custom Metrics, and Baseline Comparison.

In AppDynamics Application Performance Management (APM), two types of Data Collectors are SQL data collectors and Method invocation data collectors. SQL data collectors capture and record detailed information about SQL queries executed by the application, helping identify slow or inefficient database operations. Method invocation data collectors capture information about specific method calls within the application code, including execution times and parameters, providing deep insights into code-level performance.

AppDynamics dashboard metadata is archived in JSON format. JSON preserves the dashboard’s structural and configuration information, including widget definitions, layout, metric references, display settings, data sources, labels, and other properties required to reconstruct the dashboard.

This format is appropriate because dashboard metadata is hierarchical configuration data rather than a flat report. A JSON archive can retain nested relationships among dashboard objects and can be exported, stored in source control, transferred between compatible Controller environments, or used as part of backup and migration procedures.

PDF is intended for rendering a visual report and does not preserve editable dashboard configuration. CSV and XLSX are tabular formats suitable for exporting metric or report data, but they cannot accurately represent the complete object structure and layout of a custom dashboard.

Therefore, when the objective is to archive the dashboard definition itself rather than merely capture its displayed output, JSON is the correct format.

Relevant CAAPA Study Guide topics include Custom Dashboards, Dashboard Import and Export, Metadata Archiving, JSON Configuration, Controller Migration, and Dashboard Backup Procedures.

When configuring Automatic Cleanup of stale Business Transactions, the " minutes since creation " option is often paired with the " Number of Calls " metric. This setting ensures that business transactions that have not been called within a certain timeframe and have had a negligible number of calls are automatically cleaned up, helping to maintain an organized and relevant set of business transactions in the monitoring system.

To plot Host CPU and GC (Garbage Collection) Time Spent in a single view, the " JMX tab under ' Tiers and Nodes ' " and the " Metrics Browser " are the appropriate methods. The JMX tab provides access to Java Management Extensions (JMX) metrics, including those related to GC time. The Metrics Browser allows for the customization and aggregation of various metrics, including Host CPU usage and GC metrics, enabling a combined view of these critical performance indicators.