500-420 Cisco AppDynamics Associate Performance Analyst Question and Answers

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.

References:

• AppDynamics documentation on Server Tagging: https://docs.appdynamics.com/latest/en/infrastructure-visibility/infrastructure-visibility-settings/server-tags

If a Business Transaction stops being displayed on the Business Transaction Dashboard after continuous load, it could be due to the deletion of the Business Transaction Custom Match Rule. Custom Match Rules in AppDynamics are used to define custom business transactions based on specific criteria. If such a rule is deleted, transactions that were previously identified and displayed based on that rule may no longer be recognized as distinct business transactions, leading to their disappearance from the dashboard.

References:

• AppDynamics documentation on Business Transactions: Provides insights on configuring and managing business transactions, including the use of custom match rules.

The performance pattern outside of business hours on 6/16/18 that would be of most interest to a Performance Analyst is the elevated response time, as indicated on 6/19/18. This is because it shows a significant spike in response time, which could indicate a performance issue that needs to be addressed. Anomalies in response time can often be more indicative of underlying problems than changes in call volume, especially when they occur outside of expected peak periods.

References:

• AppDynamics documentation on Transaction Score: https://docs.appdynamics.com/latest/en/application-monitoring/application-dashboard/transaction-score

When a Performance Analyst is faced with issues related to transaction response times on a specific node, focusing on the "Transaction performance" data is most relevant. This is because transaction performance metrics provide direct insights into the business transactions' speed, response times, and execution times, which are critical for identifying and troubleshooting performance issues. By selecting "Transaction performance" when creating a health rule, the analyst can monitor specific metrics such as average response time, slow transaction count, and very slow transaction count, which are directly related to the performance of business transactions on the node in question.

References:

• AppDynamics documentation on Health Rules: This section explains how to create and configure health rules to monitor application performance metrics, including transaction performance.
• AppDynamics documentation on Business Transactions: Provides details on how AppDynamics identifies and monitors business transactions, which are key to understanding application performance.

Key Performance Indicators (KPIs) such as "CPU %Busy" and "Memory Used%" are critical for providing insights into server-level resource consumption. "CPU %Busy" indicates the percentage of time the CPU is actively working on processes, reflecting the server's processing workload. "Memory Used%" shows the proportion of memory utilized, indicating how much of the server's RAM is being consumed by applications and processes. These KPIs are essential for understanding and managing server performance and resource allocation.

References:

• AppDynamics documentation on Server Monitoring: Includes information on monitoring server-level metrics, including CPU and memory utilization, to assess resource consumption.

The Business Transaction Discovery feature in AppDynamics allows users to identify new and potential business transactions directly within the Business Transaction Dashboard. This feature aids in the continuous monitoring and adaptation of the application's transaction tracking to changing business needs, ensuring comprehensive visibility. Users can also preview these identified transactions, assessing their relevance and impact on the application's performance before deciding to monitor them as official business transactions.

References:

• AppDynamics documentation on Business Transaction Monitoring: Discusses how to manage and monitor business transactions, including the discovery and preview of new or potential transactions.
• AppDynamics documentation on Business Transaction Dashboard: Explains the functionalities and features of the Business Transaction Dashboard, including transaction discovery.

Both Service Endpoints and Business Transactions in AppDynamics have the capability to configure data collectors. Data collectors are used to gather detailed information about transactions or endpoints, such as method parameters, return values, and SQL statements.

References:

• AppDynamics documentation on Data Collectors: https://docs.appdynamics.com/latest/en/application-monitoring/configure-data-collection

In the Browser Real User Monitoring (RUM) Dashboard, the Usage Stats section provides insights into user demographics, including the types of browsers and devices they are using. This section would help the Performance Analyst understand which browsers and devices are affected during a specific time period when there has been an increase in the Business Transaction error rate.

References:

• AppDynamics documentation on Browser RUM: https://docs.appdynamics.com/21.6/en/end-user-monitoring/browser-monitoring/browser-real-user-monitoring

The primary concern for a Performance Analyst when considering the impact of automatic diagnostic sessions is the application itself. Intensive diagnostic sessions can be resource-heavy, potentially affecting the application's performance. Therefore, it is often necessary to limit the duration and frequency of these sessions to ensure they do not negatively impact the application’s end-user experience.

References:

• AppDynamics documentation on Diagnostic Sessions: https://docs.appdynamics.com/latest/en/troubleshoot/diagnostic-sessions

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.

When configuring an Information Point in AppDynamics, you can add match conditions to refine what gets measured. Match conditions based on a regex applied to the method allow you to specify which methods to include based on a regular expression pattern. Matching based on the invoked object allows you to specify which objects' methods are included, filtering the data according to the object type or instance. These conditions help in pinpointing specific methods or objects for which you want to collect runtime information.References:AppDynamics documentation on Information Points and Match Conditions.

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.

References:

• AppDynamics documentation on Business Transactions: Discusses best practices for managing business transactions, including the automatic cleanup based on time and call volume thresholds.

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.

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.

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.

References:

• AppDynamics documentation on Business Transaction Management: Includes information on Business Transaction Discovery Sessions and the use of Live Preview to validate Transaction Discovery Rules.

A transaction snapshot in AppDynamics is a detailed report of a single execution of a business transaction. Its primary purpose is to analyze issues with a specific business transaction by providing a comprehensive view of the transaction's execution path, including timing, call graphs, and database queries. This allows performance analystsand developers to drill down into individual transactions to diagnose performance bottlenecks, errors, or anomalies.

References:

• AppDynamics documentation on Transaction Snapshots: Offers detailed guidance on how to capture and analyze transaction snapshots to troubleshoot and optimize application performance.