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| Section | Weight | Objectives |
|---|---|---|
| Agents and Application Performance | 20% | - Troubleshoot performance problems - Monitor performance trends - Configure alerts and identify system performance metrics - Determine application performance impact and scheduled reports - Verify agent functionality, performance, and connectivity |
| Collectors, Information Points, and Service Endpoints | 25% | - Configure information points and service endpoints - Analyze service endpoint metrics - Define and configure data collectors |
| Business Transactions | 20% | - Manage discovery and analyze transaction metrics - Create and configure business transaction settings - Determine transaction scope and rule assignment |
| Troubleshooting | 15% | - Capture snapshots and troubleshoot health rule violations - Identify slow performance and errors from backend systems - Utilize dashboards to identify performance issues |
| Health Rules, Dashboards, and Snapshots | 20% | - Create and configure health rules - Locate and analyze snapshots - Create and interpret dashboards |
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NEW QUESTION # 42
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: C
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 # 43
Which Application Dashboard view categorizes transactions by load, response time, errors, slow transactions, and stalled transactions in a single aggregated value for a specific time range?
Answer: B
Explanation:
The Transaction Score view in the Application Dashboard categorizes transactions by load, response time, errors, slow transactions, and stalled transactions. It provides an aggregated value for a specific time range, giving an at-a-glance indication of the health and performance of business transactions.
References:
AppDynamics documentation on Transaction Score: https://docs.appdynamics.com/latest/en/application- monitoring/application-dashboard
NEW QUESTION # 44
Developers and operations personnel complain that the Controller tracks many irrelevant Java classes and methods. How does a Performance Analyst exclude this unhelpful information?
Answer: C
Explanation:
The Performance Analyst should navigate to Configuration > Instrumentation > Call Graph Settings and configure package exclusions. Call Graph Settings control which Java packages, classes, and methods the Application Agent includes when constructing call graphs for transaction snapshots.
Excluding irrelevant package prefixes removes low-value framework, utility, generated-code, or third-party method detail from captured call graphs. This reduces diagnostic noise, makes transaction snapshots easier for developers to interpret, and can lower instrumentation and data-collection overhead. Exclusions should be applied carefully so that application-owned packages and methods required for root-cause analysis remain visible.
The controller-info.xml file contains Java Agent registration and Controller connectivity settings, such as the Controller address, account information, application, tier, and node identity. It is not the appropriate location for call-graph package exclusions. Baselines evaluate normal metric behavior and have no effect on instrumented classes or methods. The mbeans-server.xml file concerns JMX or MBean-related monitoring rather than Java call-graph filtering.
Relevant CAAPA Study Guide topics include Instrumentation Configuration, Call Graph Settings, Package Exclusions, Java Agent Overhead Management, Transaction Snapshots, and Code-Level Visibility.
NEW QUESTION # 45
A Performance Analyst is evaluating the performance of a shopping application from a customer perspective.
Which three Key Performance Indicators (KPIs) are quantifiable measurements used to determine user experience? (Choose three.)
Answer: A,D,E
Explanation:
The three KPIs that most directly quantify customer experience are Business Transactions 94th Percentile Response Time, End User Response Time, and Business Transactions Errors per Minute.
The 94th percentile response time indicates how long the majority of Business Transaction executions take while reducing the masking effect of averages. It reveals whether a meaningful portion of customers is experiencing slow checkout, search, cart, or order-processing operations.
End User Response Time measures the customer-observed time required for a browser or mobile interaction to complete. It includes client-side processing, network latency, content loading, and server response, making it the most direct representation of perceived application performance.
Business Transactions Errors per Minute quantifies failed application operations. A rising error rate can represent unsuccessful logins, payment failures, rejected orders, or other outcomes that directly impair the customer journey.
Network incoming errors, Machine Agent availability, and CPU utilization are valuable infrastructure indicators, but they measure supporting-system health rather than customer experience itself.
Relevant CAAPA Study Guide topics include End User Monitoring, Business Transaction Performance, Percentile Response Time, Error Monitoring, Customer Experience KPIs, and Application Performance Correlation.
NEW QUESTION # 46
Which data type can be collected as custom data by an Information Point?
Answer: C
Explanation:
An AppDynamics Information Point can collect String values as custom data. Information Points instrument selected application methods so that analysts can monitor method-level performance and capture business context without defining the method as a complete Business Transaction.
Custom data is typically extracted from a method argument, return value, object field, or getter chain. The resulting String value can provide contextual information such as an order category, customer type, transaction status, product identifier, or processing state. This data is associated with the monitored method execution and can support detailed troubleshooting and business-level analysis.
Numeric values such as integers or floating-point numbers are generally treated as candidates for custom metrics, where AppDynamics can aggregate and report values over time. Boolean values are not the designated custom-data type for this Information Point configuration. The distinction is therefore between textual custom data, represented as a String, and numerical measurements intended for metric aggregation.
Relevant CAAPA Study Guide topics include Information Points, Method-Level Monitoring, Getter Chains, Custom Data Collection, Custom Metrics, Method Arguments, Return Values, and Business Context Capture.
NEW QUESTION # 47
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