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| Section | Weight | Objectives |
|---|---|---|
| Business Transactions | 25% | - Metrics analysis and baselining - Slow, error, and stalled transaction identification - Transaction detection and configuration |
| Information Points, Collectors, and Service Endpoints | 20% | - Service endpoint monitoring - Database and custom collectors - Configuring information points |
| Flow Maps and Application Architecture | 20% | - Component dependency visualization - Interpreting flow maps - Environment and topology overview |
| Troubleshooting and Performance Analysis | 15% | - Analyzing call graphs and snapshots - Remediation and optimization steps - Identifying bottlenecks and root causes |
| Health Rules, Alerts, and Dashboards | 20% | - Creating and managing health rules - Alert configuration and actions - Custom dashboards and reporting |
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NEW QUESTION # 108
Which two roll-up types can be configured for an Information Point? (Choose two.)
Answer: A,D
Explanation:
An Information Point custom metric can be configured with the roll-up types Average and Sum. The selected roll-up determines how values reported by repeated executions of the monitored class and method are aggregated during each metric reporting interval.
Average calculates the mean of all collected values within the interval. This is appropriate for measurements such as average item-processing time, average order value, or another numeric value where typical behavior is the primary concern.
Sum adds all collected values reported during the interval. It is appropriate for cumulative business or operational measurements, such as the total number of items processed, total transaction value, or total quantity handled by the instrumented method.
Max is not one of the configurable Information Point roll-up choices in this context. Although maximum values can appear in other AppDynamics metric-analysis scenarios, they are not selected as an Information Point custom-metric roll-up type. Count is represented separately through invocation-frequency metrics such as Calls per Minute rather than as the configured roll-up method for the collected numeric value.
Relevant CAAPA Study Guide topics include Information Points, Custom Metrics, Metric Aggregation, Average Roll-Up, Sum Roll-Up, Method Invocation Monitoring, and Calls per Minute.
NEW QUESTION # 109
Which two methods are used to plot Host CPU and GC Time Spent in a single view? (Choose two.)
Answer: C,D
Explanation:
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. TheJMX 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.
References:
AppDynamics documentation on Monitoring Tiers and Nodes: Discusses the JMX metrics available for Java applications, including garbage collection details.
AppDynamics documentation on the Metrics Browser: Describes how to use the Metrics Browser to view and analyze a wide range of performance metrics.
NEW QUESTION # 110
Which type of Data Collector will capture code data such as method arguments, variables, and return values?
Answer: B
Explanation:
The " Method Invocation Data Collector " is specifically designed to capture code-level data such as method arguments, variables, and return values. This type of data collector enables deep visibility into the execution of methods within transactions, providing valuable insights into the application ' s behavior and performance.
This detailed level of monitoring is essential for diagnosing complex issues and understanding the inner workings of business transactions.
References:
AppDynamics documentation on Data Collectors: Details the types of data collectors available, including Method Invocation Data Collectors, and how they can be used to capture detailed code-level data.
NEW QUESTION # 111
When should URI Segments be used in Transaction Detection rules?
Answer: D
Explanation:
URI Segments should be used in Transaction Detection rules when the application is web-oriented.
Web-oriented applications often have meaningful information in the URIthat can identify different business transactions. URI Segments can help in defining patterns that match specific parts of the URI to distinguish different transactions.
References:
AppDynamics documentation on Transaction Detection:
https://docs.appdynamics.com/latest/en/application-monitoring/business-transactions
NEW QUESTION # 112
A Performance Analyst is reviewing Business Transactions with an Application team. The Application team would like to increase the Application Business Transaction limit because they need to have visibility into all the different transactions. What should the Performance Analyst do?
Answer: B
Explanation:
When an application team requests an increase in the Application Business Transaction limit for visibility purposes, it's crucial for the Performance Analyst to focus on the transactions that are currently exceeding the limit and understand why. This approach helps in identifying whether the limit is being reached due to genuinely essential transactions or if there are redundant, irrelevant, or improperly defined transactions contributing to the limit breach. By analyzing and rationalizing the transactions, the analyst can ensure that only valuable transactions are monitored, optimizing resource usage and maintaining effective observability without necessarily increasing the limit.
References:
AppDynamics documentation on Business Transaction Limits: Discusses the implications of business transaction limits and strategies for managing and optimizing these limits within AppDynamics.
AppDynamics Best Practices Guide: Offers recommendations for configuring and managing business transactions, including handling limits and ensuring meaningful transaction monitoring.
NEW QUESTION # 113
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