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| Section | Weight | Objectives |
|---|---|---|
| Business Transactions | 25% | - Metrics analysis and baselining - Transaction detection and configuration - Slow, error, and stalled transaction identification |
| Troubleshooting and Performance Analysis | 15% | - Analyzing call graphs and snapshots - Identifying bottlenecks and root causes - Remediation and optimization steps |
| Health Rules, Alerts, and Dashboards | 20% | - Alert configuration and actions - Custom dashboards and reporting - Creating and managing health rules |
| Information Points, Collectors, and Service Endpoints | 20% | - Configuring information points - Service endpoint monitoring - Database and custom collectors |
| Flow Maps and Application Architecture | 20% | - Environment and topology overview - Component dependency visualization - Interpreting flow maps |
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NEW QUESTION # 55 
Refer to the exhibit. When looking at the Transaction Score for a specific transaction, how are errors in the transaction identified?
Answer: B
Explanation:
Errors in a transaction are identified by examining the snapshots that capture the problematic transactions. By setting the appropriate time range, a Performance Analyst can drill down into the snapshots within the Error tab to identify and analyze errors. These snapshots provide detailed diagnostic information, such as stack traces, slow SQL queries, and error logs, which are vital for pinpointing the root cause of transaction errors.
References:
AppDynamics documentation on Transaction Snapshots:
NEW QUESTION # 56
In which two features of AppDynamics can Information Points metric data be used? (Choose two.)
Answer: C,D
Explanation:
Information Points in AppDynamics are custom metrics that track specific data within your applications, such as method invocations or the value of method arguments. These metrics can be utilized in various features of AppDynamics, most notably in "Alerting" and "Custom Dashboards." Alerting allows you to set up notifications based on the thresholds set for Information Points, ensuring that teams are promptly informed about significant changes or anomalies. Custom Dashboards enable the visualization of Information Points metrics alongside other key performance indicators, providing a comprehensive view of application health and performance tailored to specific needs.
References:
AppDynamics documentation on Information Points: Explains how to create and use Information Points to monitor specific business-relevant metrics.
AppDynamics documentation on Alerting: Details the process of setting up health rules and alerts based on various metrics, including those from Information Points.
AppDynamics documentation on Custom Dashboards: Guides on how to create dashboards that incorporate a wide range of metrics, including Information Points, for customized monitoring.
NEW QUESTION # 57
An online order portal for a restaurant has a "menu" transaction with different request types that must be monitored as separate transactions, such as "menu.salads" and "menu.entree." A Performance Analyst wants to create one rule that separates the transactions instead of creating an individual rule for each request type.
Which option should be enabled on a Custom Match rule?
Answer: D
Explanation:
The Performance Analyst should enable Split Transaction using request data. This capability allows a single Custom Match rule to dynamically divide one base transaction into multiple business transactions according to values contained in the incoming HTTP request. For example, the rule can identify the base transaction as menu and append a request-derived value such as salads or entree, producing distinct transactions named menu.salads and menu.entree.
This approach avoids maintaining separate match rules for every menu category and scales automatically as additional request values are introduced.
An HTTP Request Data Collector captures request attributes for visibility within transaction snapshots but does not itself split or rename business transactions. A Method Invocation Data Collector captures method parameters, return values, or invoked-object data and is also unrelated to transaction splitting. Split Transaction using JSON is appropriate only when the required discriminator must specifically be extracted from a JSON payload.
Relevant CAAPA topics include Business Transaction Detection, Custom Match Rules, Transaction Naming, Request-Based Splitting, and Data Collectors.
NEW QUESTION # 58
A Performance Analyst has a third-party event management system API and wants to use the event management system to send alerts to Level 1.
Which actions are available in the AppDynamics Actions Menu to set this up?
Answer: D
Explanation:
When integrating with a third-party event management system, the Performance Analyst can set up an HTTP Request action within AppDynamics. This allows for the creation of a custom HTTPrequest to the event management system's API, which can be configured to send alerts to Level 1 support or any other designated recipient.
NEW QUESTION # 59
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 # 60
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