Information Points in AppDynamics are designed to collect custom metrics that are specific to the

business or code aspects of an application. They can capture Business Metric Data, which pertains to

the performance metrics that directly impact business processes, and Code Metric Data, which

relates to the performance of specific methods or segments of code within the application. This

allows for targeted monitoring and analysis of areas significant to the business's objectives and

technical performance.

Reference:

AppDynamics documentation on Information Points: Provides details on how to set up Information

Points to collect custom business and code metrics for in-depth performance analysis.

AppDynamics allows for several reports to be scheduled for periodic generation and sent to email

recipients. Among these, Application Health, Error Trends, and Custom Dashboards are types of

reports that can be automatically generated and delivered, providing regular insights into the

application's performance and issues.

Reference:

AppDynamics documentation on Reports: https://docs.appdynamics.com/latest/en/applicationmonitoring/reporting

The default value pair for the setting "Collect up to ____ snapshots per minute for _____ minutes" in

the configuration for Slow Transactions is 10, 10. This means that by default, the system is set to

collect up to 10 snapshots per minute for a duration of 10 minutes during a diagnostic session.

Reference:

AppDynamics documentation on Transaction Snapshots: Outlines the default settings for diagnostic

session duration and collection rates, including the collection of snapshots.

If the Average Response Time is increasing after the release of a new marketing offer, seeing an

increase in Calls per Minute could quickly help the Performance Analyst determine that users may

not be critically impacted. This increase may indicate that despite the higher average response time,

more users are engaging with the application, possibly due to interest generated by the marketing

offer. It suggests that the system is handling more load, which could be a factor in the increased

response time.

Reference:

AppDynamics documentation on Performance Metrics: Discusses the implications of various key

performance indicators, including how an increase in Calls per Minute can be interpreted in the

context of application performance.

Server Tagging in AppDynamics allows a Performance Analyst to compare and filter related servers

by using custom metadata. This feature enables users to group and organize their servers based on

criteria that make sense for their environment, such as role, location, environment type, or any other

custom metadata.

Reference:

AppDynamics documentation on Server Tagging:

https://docs.appdynamics.com/latest/en/infrastructure-visibility/infrastructure-visibilitysettings/server-tags

In Cisco AppDynamics, the transaction threshold configurations are typically found under the

"Overview" tab. This is where you can view the health rule violations and performance baselines that

are associated with transaction snapshots, which can include the configured transaction thresholds.

These thresholds set the acceptable performance limits for transactions, and when these limits are

exceeded, it may trigger health rule violations that are visible on the Overview tab.

Reference:

AppDynamics Documentation on Transaction Snapshots

AppDynamics Documentation on Health Rule Violations

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.

Reference:

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.

The Live Preview option in a Business Transaction Discovery Session in AppDynamics allows a user to

verify the effectiveness of newly configured Transaction Discovery Rules. This feature provides real-

time feedback on how these rules identify and categorize business transactions, enabling users to

adjust and fine-tune the rules to ensure accurate and meaningful transaction monitoring.

Reference:

AppDynamics documentation on Business Transaction Management: Includes information on

Business Transaction Discovery Sessions and the use of Live Preview to validate Transaction

Discovery Rules.

When creating Information Points in AppDynamics, wildcards can be utilized to select multiple

methods or classes, thereby broadening the scope of data collection. This feature is particularly

useful for monitoring similar operations across different components of an application, enabling a

more efficient and scalable approach to gathering custom metrics.

Reference:

AppDynamics documentation on Information Points: Elaborates on the process of setting up

Information Points, including the use of wildcards to capture metrics from multiple methods or

classes.

For an e-commerce application built using a microservices architecture, logically grouping

Transaction Detection rules can be effectively achieved through "Use Transaction Group." This

approach allows for the organization of business transactions into meaningful groups that reflect the

application's structure and the interactions between its microservices. By grouping transactions, it

becomes easier to monitor, analyze, and troubleshoot the application as a whole and its individual

components, enhancing the visibility and management of the application's performance.

Reference:

AppDynamics documentation on Business Transactions: Provides insights on how to configure and

manage business transactions, including grouping and monitoring strategies.

AppDynamics documentation on Microservices Monitoring: Offers guidance on best practices for

monitoring applications designed with microservices architecture, including transaction grouping.