500-420 Cisco AppDynamics Associate Performance Analyst(CAAPA) Questions and Answers

The Metric Explorer in AppDynamics allows Performance Analysts to define and visualize Key Performance Indicators (KPIs) from a selection of metrics. By accessing the Transaction Analytics data set, analysts can create custom dashboards that focus on the metrics they've determined to be critical KPIs for their application's performance.

References:

• AppDynamics documentation on Metric Explorer:

When new business transactions are not appearing on the Business Transaction Dashboard after deploying new code, developers should verify that there are no exclusion rules in place on the tier where the new code was deployed. Additionally, it is crucial to ensure that the Auto Discovery feature for servlets is enabled for Java agents, as this allows AppDynamics to automatically detect and name business transactions based on incoming requests to servlets. Both of these checks are necessary to ensure that new business transactions can be discovered and monitored.References:AppDynamics documentation on Business Transaction detection and Java Agent configuration.

Development Level Monitoring in AppDynamics is designed for use in a lower volume, non-production environment. For a default configuration, Development Level Monitoring will get automatically disabled if the monitored environment exceeds a threshold, typically a maximum of 1000 calls per minute, and the Maximum heap utilization percentage goes above 95%. These thresholds are in place to prevent excessive overhead in a production environment where such detailed monitoring could impact performance.

References:

• AppDynamics documentation on Agent Configuration: Provides information on configuration settings for monitoring levels, including when Development Level Monitoring is automatically disabled due to exceeding thresholds.

Controller-level and tier/node-level dashboards in AppDynamics are treated as separate entities. They are scoped differently, with controller-level dashboards providing a global view across the entire AppDynamics domain, and tier/node-level dashboards being specific to particular tiers or nodes within an application. Performance Analysts do not have the ability to cross-reference directly between these two sets of dashboards within the AppDynamics UI.

References:

• AppDynamics documentation on Dashboards: https://docs.appdynamics.com/latest/en/application-monitoring/custom-dashboards

In environments where virtual machines are frequently created and destroyed, using the agent configuration properties-Dappdynamics.agent.reuse.nodeNameand-Dappdynamics.agent.reuse.nodeName.prefixcan be beneficial. These settings allow AppDynamics agents to reuse node names for newly created instances, facilitating consistent monitoring and tracking across dynamic environments. This approach helps maintain clarity in application topology and ensures that performance data remains coherent even as the underlying infrastructure changes.

References:

• AppDynamics documentation on Agent Configuration: Discusses advanced agent configuration options, including node name reuse, to accommodate dynamic environments such as those with frequently changing virtual machines.
• AppDynamics documentation on Infrastructure Monitoring: Offers insights into best practices for monitoring in dynamic and virtualized environments.

Backend calls in AppDynamics are the interactions that an application component has with external components or services. These can include calls to databases, remote service calls, and interactions between different tiers of an application.

• A tier-to-tier request refers to any internal call that happens between different tiers (or nodes) within the same application. For example, a web tier calling an API service tier within the same application ecosystem.
• A remote services call is an external call from an application to a service that resides outside of the application's environment, like a call to an external web service, REST API, or a third-party service provider.

References:

• AppDynamics documentation on Backend Detection: https://docs.appdynamics.com/21.6/en/application-monitoring/identify-backends

When a Performance Analyst modifies an existing health rule that is already in a state of critical violation, the event that is typically triggered is "Health Rule Violation Continues - Critical." This event indicates that, despite the modification, the health rule is still being violated at a critical level. The system recognizes that the conditions for the health rule violation are still being met and continues to alert accordingly.

References:

• AppDynamics documentation on Health Rules and Events: Explains the different types of health rule events and the conditions under which they are triggered.

AppDynamics imposes a limit on the number of Service Endpoints that can be registered per node to ensure manageable performance and overhead. The limit per node is set to 100 Service Endpoints, which is a balance between providing detailed monitoring and maintaining application performance.

References:

• AppDynamics documentation on Service Endpoints: https://docs.appdynamics.com/latest/en/application-monitoring/monitor-service-endpoints

Creating a Transaction Group using the 'Create Group' action in AppDynamics allows for the aggregation of data from multiple transactions under a single group, facilitating a consolidated view of similar transactions. This differs from defining a Transaction Detection Rule, which typically focuses on identifying and categorizing individual transactions based on specific criteria. Transaction Groups do not change the names of incoming requests nor reduce the number of overall business transactions; instead, they provide a method for organizing and analyzing related transactions collectively, offering a streamlined approach compared to individually configuring Transaction Detection Rules for each transaction.

References:

• AppDynamics documentation on Business Transactions: Covers the concept of Transaction Groups and their role in organizing and analyzing transaction data, as well as the process for creating and managing Transaction Detection Rules.

To share snapshot details from the snapshot window in AppDynamics, the options to "Download PDF" and "Export" are typically used. "Download PDF" allows users to generate a PDF report of the snapshot details, providing a convenient and portable format for sharing and review. The "Export" option enables the extraction of snapshot data in various formats (e.g., CSV, XML), facilitating further analysis or sharing with other tools and stakeholders.

References:

• AppDynamics documentation on Snapshots: This section covers how snapshots capture detailed performance data for transactions, including how to share and export this information for collaborative analysis.

When Business Transaction Lock Down is enabled in AppDynamics, no new business transactions will be automatically discovered to avoid uncontrolled growth in the number of business transactions. To track a new service as a unique Business Transaction, one needs to create a Custom Match Rule that specifies the criteria for identifying the business transaction. In this case, the Custom Match Rule should be based on the Web Service Name and Operation Name which are the distinguishing characteristics of the new service. This allows for the precise identification and monitoring of the service within the AppDynamics platform.References:AppDynamics documentation on Business Transaction configuration and Custom Match Rules.

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.

References:

• AppDynamics documentation on Data Collectors: Provides detailed information on configuring SQL and Method invocation data collectors for in-depth application monitoring.

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.

When an Information Point is created in AppDynamics without adding custom data, it automatically collects three key performance indicators (KPIs): Response Time, Errors per Minute, and Calls per Minute. Response Time measures the time taken to complete a transaction or operation, providing insights into application performance. Errors per Minute tracks the number of errors occurring within the scope of the Information Point, helping identify problematic areas. Calls per Minute counts the number of times the specified operation or transaction is invoked, indicating its usage frequency and potential impact on application performance.

References:

• AppDynamics documentation on Information Points: Discusses the creation and configuration of Information Points, including the default metrics collected.

Data collectors in AppDynamics supplement business transaction and transaction analytics data with application data by capturing additional information during transaction execution. This can include method arguments, return values, and invocation context, which enriches the transaction data with more detailed application-level insights. This capability is crucial for in-depth performance analysis and troubleshooting, providing a more comprehensive view of application behavior.

References:

• AppDynamics documentation on Data Collectors: Explains how to configure data collectors to capture and display detailed application data within business transactions.

To streamline the monitoring process and enhance the relevance of collected data, AppDynamics allows Performance Analysts to exclude certain Java classes and methods from instrumentation. This is done through the "Configuration > Instrumentation > Call Graph Settings" menu, where irrelevant packages and classes can be specified for exclusion. This approach prevents the AppDynamics agent from tracking unnecessary or irrelevant methods, thus reducing overhead and focusing on the most critical aspects of application performance.

References:

• AppDynamics documentation on Call Graph Settings: This section details how to configure call graph settings, including the exclusion of specific classes and methods from instrumentation, to optimize performance monitoring.