Refer to the exhibit.

Refer to the exhibit. When looking at the Transaction Score for a specific transaction, how are errors in the transaction identified?
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.
AppDynamics documentation on Transaction Snapshots:
Using the Business Transaction Discovery Sessions tools, which two items can a Performance Analyst browse? (Choose two.)
Business Transaction Discovery Sessions allow a Performance Analyst to browse the application's classes and methods while identifying suitable entry points for custom Business Transaction detection. The discovery process observes application execution and exposes loaded or invoked code structures, enabling the analyst to locate the class and method that represent the beginning of a meaningful transaction.
Browsing classes helps narrow the search to the relevant package or application component. After selecting a class, the analyst can inspect its methods and choose the appropriate method as the transaction entry point. This is particularly useful when automatic discovery does not recognize a proprietary framework, background process, scheduled task, or other nonstandard execution path.
Database Calls are analyzed as exit calls or backend activity rather than browsed as transaction entry-point code. JMX MBeans are accessed through JMX monitoring and metric configuration tools. Memory Objects are examined through memory diagnostics, object instance tracking, or leak-detection functions, not Business Transaction Discovery Sessions.
Relevant CAAPA Study Guide topics include Business Transaction Discovery Sessions, Class and Method Browsing, Custom Match Rules, Entry-Point Configuration, Automatic Discovery, and Application Agent Instrumentation.
A Performance Analyst is creating a custom baseline and attempting to tag it to a widget in a dashboard. Which statement is true?
AppDynamics supports the creation of custom baselines, allowing a Performance Analyst to define expected metric behavior over a selected historical period and recurring time pattern. Custom baselines are useful when the standard daily, weekly, or trend-based baselines do not accurately represent the application's operating schedule, seasonal demand, or business cycle.
However, a custom baseline cannot be directly tagged or bound to an individual dashboard widget. Dashboard widgets can visualize metrics and, depending on the widget type, may display standard comparison or baseline-related information, but they do not provide a mechanism for associating a specifically named custom baseline with that widget.
Custom baselines are primarily applied in monitoring and evaluation contexts such as health-rule conditions, where current metric values can be compared against expected behavior using defined baseline criteria and standard deviations. Therefore, the baseline itself can be created and used for performance evaluation, but the analyst's attempt to attach it directly to a dashboard widget is unsupported.
Relevant CAAPA Study Guide topics include Dynamic Baselines, Custom Baseline Configuration, Health Rule Conditions, Baseline Evaluation, Metric Visualization, and Custom Dashboard Widget Limitations.
On a snapshot flow map, which three metrics indicate why the snapshot might have been taken? (Choose three.)
A snapshot flow map presents execution-specific timing information that helps explain why AppDynamics captured that particular transaction instance. The relevant metrics are Thread Blocking Time, Execution Time, and Asynchronous Activity Processing Time.
Execution Time shows the elapsed processing time for the transaction segment and helps determine whether the request exceeded slow or very slow transaction thresholds. Thread Blocking Time identifies time during which the executing thread was blocked or waiting, potentially because of lock contention, synchronization, resource availability, or another thread-related delay. A substantial blocking value can explain why an otherwise ordinary code path produced a slow snapshot.
Asynchronous Activity Processing Time represents time consumed by asynchronous work associated with the transaction, such as executor tasks, callbacks, or background processing correlated with the originating request. Excessive asynchronous processing can materially increase end-to-end completion time and trigger snapshot collection.
Tier Response Time and Node Response Time are aggregated performance metrics across multiple transactions. They provide useful operational context but do not directly describe the timing characteristics of one captured transaction execution.
Relevant CAAPA Study Guide topics include Transaction Snapshots, Snapshot Flow Maps, Execution Time, Thread Blocking, Asynchronous Transaction Tracking, and Slow Transaction Diagnostics.
Developers and operations personnel complain the Controller tracks many irrelevant Java classes and methods. How does a Performance Analyst exclude this unhelpful information?
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.
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.
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