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| Section | Objectives |
|---|---|
| Topic 1: AppDynamics Architecture | - Controller and agents
|
| Topic 2: Diagnostics and Troubleshooting | - Root cause analysis
|
| Topic 3: AppDynamics Fundamentals | - APM concepts and terminology
|
| Topic 4: Monitoring and Instrumentation | - Infrastructure monitoring
|
| Topic 5: Alerting and Business Performance | - Business transaction performance
|
| Topic 6: Dashboards and Reporting | - Performance dashboards
|
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NEW QUESTION # 21
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: B,C,F
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 # 22
Which path is used to specify a metric that evaluates across multiple hardware entities or memory pools?
Answer: B
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 # 23
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: B
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 # 24
What AppDynamics Alert Action does a Performance Analyst need to select to post an AppDynamics event to a third-party collaboration tool?
Answer: B
Explanation:
To post an AppDynamics event to a third-party collaboration tool, the Performance Analyst needs to select the
"Make an HTTP Request" alert action. This action allows AppDynamics to send an HTTP request to a specified URL, which can be the endpoint provided by the third-party tool's API. This integration capability enables the automatic posting of events, alerts, and notifications from AppDynamics to collaboration tools, enhancing communication and response times to performance issues.
References:
AppDynamics documentation on Alert and Respond: Details the various alert actions available within AppDynamics, including the ability to make HTTP requests to integrate with external systems.
NEW QUESTION # 25
Which values can be used to identify a split exit point?
Answer: B
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 # 26
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