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| Section | Objectives |
|---|---|
| Topic 1: AppDynamics Fundamentals | - APM concepts and terminology
|
| Topic 2: Dashboards and Reporting | - Performance dashboards
|
| Topic 3: Alerting and Business Performance | - Business transaction performance
|
| Topic 4: AppDynamics Architecture | - Controller and agents
|
| Topic 5: Diagnostics and Troubleshooting | - Root cause analysis
|
| Topic 6: Monitoring and Instrumentation | - Application instrumentation
|
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NEW QUESTION # 122
What does the Live Preview option in a Business Transaction Discovery Session allow a user to do?
Answer: B
Explanation:
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.
NEW QUESTION # 123
Which path is used to specify a metric that evaluates across multiple hardware entities or memory pools?
Answer: C
Explanation:
A relative metric path with a wildcard is used when a metric condition must be evaluated across multiple comparable entities, such as hardware resources, JVM memory pools, nodes, or other dynamically named metric branches.
A relative path is resolved in the context of each entity selected by the health rule or metric evaluation scope.
Adding a wildcard allows AppDynamics to match multiple child metric paths rather than requiring one fixed, fully qualified metric location. This is particularly useful when memory-pool names, hardware instances, or resource identifiers differ between nodes or runtime environments.
An absolute metric path points to one exact location in the metric hierarchy and is therefore better suited to a single, explicitly identified metric. Although wildcard matching can be supported in metric configurations, the correct approach for evaluating equivalent metrics across multiple scoped entities is the relative-path model.
The Metric Browser supports hierarchical metric navigation, and wildcard-capable paths are used where dynamic or repeated entity structures must be addressed. Relevant CAAPA Study Guide topics include Metric Browser, Relative and Absolute Metric Paths, Wildcard Metric Selection, Health Rule Conditions, Hardware Resource Metrics, and JVM Memory Pool Monitoring.
NEW QUESTION # 124
What must a Performance Analyst first configure in order to monitor end-to-end latency performance metrics in AppDynamics?
Answer: D
Explanation:
To monitor end-to-end latency performance metrics, a Performance Analyst must first define the end-to-end latency transaction. This involves configuring the business transactions to include the necessary entry and exit points that capture the complete flow of the transaction, thereby enabling the measurement of the total latency.
References:
AppDynamics documentation on Business Transactions: https://docs.appdynamics.com/latest/en/application- monitoring/business-transactions
NEW QUESTION # 125
What is the Node limit of the maximum Service Endpoints per node?
Answer: B
Explanation:
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
NEW QUESTION # 126
Refer to the exhibit.
Using this heap-utilization graph, which method is used to confirm whether a memory leak is occurring during a specific timeframe?
Answer: A
Explanation:
The Tiers and Nodes > Memory tab provides the appropriate JVM-level view for confirming suspected heap growth over a selected timeframe. The analyst should compare Heap Utilization (%) with Heap Current Usage (MB) versus Max (MB) and observe the effect of major garbage-collection events.
A potential memory leak is indicated when heap usage rises progressively and fails to return to a stable lower level after garbage collection. The important pattern is not simply high memory utilization, but an increasing post-garbage-collection baseline over time. The exhibit shows current heap usage gradually increasing relative to the maximum heap allocation, which warrants further memory diagnostics.
Hardware-resource memory metrics represent operating-system memory and do not isolate JVM heap behavior. Swap usage can indicate host memory pressure but cannot confirm an application heap leak. JMX metrics may expose heap values, but the dedicated Memory tab provides the quickest correlated visualization for heap utilization, maximum heap, current usage, and garbage collection.
Relevant CAAPA topics include JVM Memory Monitoring, Heap Utilization, Garbage Collection, Tiers and Nodes Memory View, Memory Leak Detection, and Object Instance Tracking.
NEW QUESTION # 127
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