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Cisco 500-420 exam is an excellent certification for individuals who want to start their career in performance analytics. Cisco AppDynamics Associate Performance Analyst certification provides the candidates with the knowledge and skills required to analyze and troubleshoot performance issues in applications and infrastructure. Cisco AppDynamics Associate Performance Analyst certification is vendor-specific and focuses on the use of Cisco AppDynamics, which is a leading application performance management solution. 500-420 exam covers topics such as performance testing, monitoring, and troubleshooting, and consists of multiple-choice questions and simulations.
Cisco 500-420 certification exam is an excellent way for professionals to demonstrate their expertise in application performance management. Cisco AppDynamics Associate Performance Analyst certification is recognized by employers around the world and can help professionals advance their careers in this field. In addition to validating their skills and knowledge, the certification can also help professionals improve their job performance by providing them with a deeper understanding of application performance management best practices and strategies.
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NEW QUESTION # 86
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 # 87
Which two Key Performance Indicators (KPIs) accurately provide insight into server-level resource consumption? (Choose two.)
Answer: C,D
Explanation:
Memory Used% and CPU %Busy are the two KPIs that directly measure server-level resource consumption.
Memory Used% represents the proportion of physical memory currently consumed on the monitored server.
A sustained high value can indicate memory pressure, excessive process allocation, insufficient capacity, or conditions that may lead to paging and degraded application performance.
CPU %Busy measures the percentage of processor capacity actively being used. It is a primary indicator of compute-resource utilization and helps identify CPU saturation, processing bottlenecks, or insufficient server capacity. Persistent high CPU utilization may correlate with increased response time, transaction queuing, and reduced throughput.
The remaining options describe availability, application behavior, or transaction performance rather than direct infrastructure-resource consumption. Calls per Minute measures workload volume, Average Response Time measures transaction latency, Availability indicates whether a system or service is reachable, and Application Restarts records lifecycle events.
Relevant CAAPA Study Guide topics include Server Visibility, Infrastructure Monitoring, Hardware Resource Metrics, CPU Utilization, Memory Utilization, and Correlating Server Health with Application Performance.
NEW QUESTION # 88
On a snapshot flow map, which three metrics indicate why the snapshot might have been taken? (Choose three.)
Answer: A,B,C
Explanation:
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.
NEW QUESTION # 89
Which two match conditions can be added when you configure an Information Point? (Choose two.)
Answer: A,C
Explanation:
When configuring an Information Point in AppDynamics, you can add match conditions to refine what gets measured. Match conditions based on a regex applied to the method allow you to specify which methods to include based on a regular expression pattern. Matching based on the invoked object allows you to specify which objects ' methods are included, filtering the data according to the object type or instance. These conditions help in pinpointing specific methods or objects for which you want to collect runtime information.
References: AppDynamics documentation on Information Points and Match Conditions.
NEW QUESTION # 90
A team of developers deploys new Java servlet code that should create new business transactions in AppDynamics. After applying load on the new code function, there are no new Business Transactions on the Business Transaction Dashboard. Whichtwo options should the developers check in AppDynamics to make sure the Business Transactions can be discovered?
[Choose two.)
Answer: D,E
Explanation:
When new business transactions are not appearing on the Business Transaction Dashboard after deploying new code, developers should verify that there are no exclusion rules in place on the tier where the new code was deployed. Additionally, it is crucial to ensure that the Auto Discovery feature for servlets is enabled for Java agents, as this allows AppDynamics to automatically detect and name business transactions based on incoming requests to servlets. Both of these checks are necessary to ensure that new business transactions can be discovered and monitored.References:AppDynamics documentation on Business Transaction detection and Java Agent configuration.
NEW QUESTION # 91
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