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| Section | Weight | Objectives |
|---|---|---|
| Business Transactions | 20% | - Create and configure business transaction settings - Determine transaction scope and rule assignment - Manage discovery and analyze transaction metrics |
| Health Rules, Dashboards, and Snapshots | 20% | - Locate and analyze snapshots - Create and configure health rules - Create and interpret dashboards |
| Troubleshooting | 15% | - Capture snapshots and troubleshoot health rule violations - Utilize dashboards to identify performance issues - Identify slow performance and errors from backend systems |
| Agents and Application Performance | 20% | - Troubleshoot performance problems - Verify agent functionality, performance, and connectivity - Configure alerts and identify system performance metrics - Determine application performance impact and scheduled reports - Monitor performance trends |
| Collectors, Information Points, and Service Endpoints | 25% | - Define and configure data collectors - Analyze service endpoint metrics - Configure information points and service endpoints |
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NEW QUESTION # 44
A Performance Analyst is experiencing difficulty with the transaction response time on a node. The Performance Analyst attempts to create a health rule monitoring the business transactions' performance on that node. Which set of data should the Performance Analyst select when creating the health rule?
Answer: A
Explanation:
When a Performance Analyst is faced with issues related to transaction response times on a specific node, focusing on the "Transaction performance" data is most relevant. This is because transaction performance metrics provide direct insights into the business transactions' speed, response times, and execution times, which are critical for identifying and troubleshooting performance issues. By selecting "Transaction performance" when creating a health rule, the analyst can monitor specific metrics such as average response time, slow transaction count, and very slow transaction count, which are directly related to the performance of business transactions on the node in question.
References:
AppDynamics documentation on Health Rules: This section explains how to create and configure health rules to monitor application performance metrics, including transaction performance.
AppDynamics documentation on Business Transactions: Provides details on how AppDynamics identifies and monitors business transactions, which are key to understanding application performance.
NEW QUESTION # 45
Which values can be used to identify a split exit point?
Answer: D
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 # 46
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: D
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 # 47
Which type of Data Collector will capture code data such as method arguments, variables, and return values?
Answer: C
Explanation:
The "Method Invocation Data Collector" is specifically designed to capture code-level data such as method arguments, variables, and return values. This type of data collector enables deep visibility into the execution of methods within transactions, providing valuable insights into the application's behavior and performance. This detailed level of monitoring is essential for diagnosing complex issues and understanding the inner workings of business transactions.
References:
AppDynamics documentation on Data Collectors: Details the types of data collectors available, including Method Invocation Data Collectors, and how they can be used to capture detailed code-level data.
NEW QUESTION # 48
Developers and operations personnel complain the Controller tracks many irrelevant Java classes and methods.
How does a Performance Analyst exclude this unhelpful information?
Answer: A
Explanation:
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.
References:
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.
NEW QUESTION # 49
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