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| Section | Weight | Objectives |
|---|---|---|
| Information Points, Collectors, and Service Endpoints | 20% | - Service endpoint monitoring - Database and custom collectors - Configuring information points |
| Troubleshooting and Performance Analysis | 15% | - Analyzing call graphs and snapshots - Identifying bottlenecks and root causes - Remediation and optimization steps |
| Business Transactions | 25% | - Metrics analysis and baselining - Slow, error, and stalled transaction identification - Transaction detection and configuration |
| Health Rules, Alerts, and Dashboards | 20% | - Alert configuration and actions - Creating and managing health rules - Custom dashboards and reporting |
| Flow Maps and Application Architecture | 20% | - Component dependency visualization - Interpreting flow maps - Environment and topology overview |
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NEW QUESTION # 26
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 URI that 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 # 27
Which two rule types are available when creating new transaction detection rules? (Choose two.)
Answer: D,E
Explanation:
AppDynamics provides two applicable rule types when defining transaction detection behavior: Exclude Rules and Custom Match Rules.
A Custom Match Rule explicitly identifies application requests that should be detected as Business Transactions. The rule can match requests by entry point, class and method, URL, servlet, framework-specific attributes, headers, parameters, or other request data. It can also control transaction naming and splitting, allowing related requests to be grouped or separated according to operational requirements.
An Exclude Rule prevents matching requests from being discovered or registered as Business Transactions.
This is useful for suppressing low-value traffic such as health checks, static-resource requests, polling calls, or framework-generated activity that would otherwise consume Business Transaction limits and add unnecessary metric noise.
An Automatic Discovery Rule is not created as a separate rule type in this context; automatic discovery is the agent's default transaction-identification behavior. A Health Rule evaluates monitored metrics and triggers policy actions, while an Include Rule is not a transaction detection rule category.
Relevant CAAPA topics include Business Transaction Detection, Custom Match Rules, Exclude Rules, Entry- Point Matching, Automatic Discovery, and Business Transaction Registration Limits.
NEW QUESTION # 28
Which values can be used to identify a split exit point?
Answer: C
Explanation:
A split exit point in AppDynamics is identified using static application values. Static values provide a consistent and predictable way to categorize exit points, making it easier to aggregate and analyze similar types of interactions with external services or components.
References:
AppDynamics documentation on Exit Points: Provides insights into how exit points are defined and identified within AppDynamics, including the use of static values for split exit points.
NEW QUESTION # 29
Which two things should a Performance Analyst check if an agent has failed to register with the Controller after several minutes? (Choose two.)
Answer: A,D
Explanation:
If an agent fails to register with the Controller, a Performance Analyst should check the network/firewall rules to ensure that the agent can communicate with the Controller over the network. Additionally, it is crucial to verify the free license count to confirm thatthere are available licenses for the agent to register. Insufficient licenses or network communication barriers can prevent successful agent registration.
References:
AppDynamics documentation on Agent Registration: Describes the prerequisites and troubleshooting steps for agent registration with the Controller.
AppDynamics documentation on Licensing: Provides details on how the licensing model works, including how to check for available licenses.
NEW QUESTION # 30
Which three Key Performance Indicators (KPIs) are automatically collected when you create an Information Point without adding custom data? (Choose three.)
Answer: A,B,C
Explanation:
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.
NEW QUESTION # 31
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