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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Information Points, Collectors, and Service Endpoints | 20% | - Database and custom collectors - Service endpoint monitoring - Configuring information points |
| Topic 2: Flow Maps and Application Architecture | 20% | - Component dependency visualization - Interpreting flow maps - Environment and topology overview |
| Topic 3: Business Transactions | 25% | - Slow, error, and stalled transaction identification - Transaction detection and configuration - Metrics analysis and baselining |
| Topic 4: Health Rules, Alerts, and Dashboards | 20% | - Custom dashboards and reporting - Creating and managing health rules - Alert configuration and actions |
| Topic 5: Troubleshooting and Performance Analysis | 15% | - Remediation and optimization steps - Identifying bottlenecks and root causes - Analyzing call graphs and snapshots |
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NEW QUESTION # 114
Which two statements are correct about creating Information Points? (Choose two.)
Answer: C,D
Explanation:
When creating Information Points in AppDynamics, wildcards can be utilized to select multiple methods or classes, thereby broadening the scope of data collection. This feature is particularly useful for monitoring similar operations across different components of an application, enabling a more efficient and scalable approach to gathering custom metrics.
References:
AppDynamics documentation on Information Points: Elaborates on the process of setting up Information Points, including the use of wildcards to capture metrics from multiple methods or classes.
NEW QUESTION # 115 
Refer to the exhibit. On which tab will the configured transaction threshold be found?
Answer: D
Explanation:
In Cisco AppDynamics, the transaction threshold configurations are typically found under the " Overview " tab. This is where you can view the health rule violations and performance baselines that are associated with transaction snapshots, which can include the configured transaction thresholds. These thresholds set the acceptable performance limits for transactions, and when these limits are exceeded, it may trigger health rule violations that are visible on the Overview tab.
References:
AppDynamics Documentation on Transaction Snapshots
AppDynamics Documentation on Health Rule Violations
NEW QUESTION # 116
Which data type can be collected as custom data by an Information Point?
Answer: D
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 # 117
What are three effects of Development Level Monitoring? (Choose three.)
Answer: A,B,D
Explanation:
Development Level Monitoring is intended for controlled development or diagnostic environments where maximum transaction visibility is more important than minimizing agent overhead. In this mode, the agent captures full call graphs, providing method-level execution detail rather than the reduced or partial call-graph collection associated with normal production monitoring.
The agent also attempts to collect a transaction snapshot for every transaction. "Attempts" is the critical distinction: snapshot creation remains subject to agent safeguards, resource constraints, transaction behavior, and collection limits. Therefore, Development Level Monitoring does not provide an absolute guarantee that every transaction will produce a retained snapshot, making option F incorrect.
Development mode additionally collects more detailed exit-call information for calls to backend systems, improving analysis of downstream databases, services, and other remote dependencies.
The agent does not ignore dev-mode-suspend-cpm. This property is a protective calls-per-minute threshold that can suspend development-mode collection when transaction volume becomes excessive. Relevant CAAPA Study Guide topics include Agent Configuration, Monitoring Levels, Transaction Snapshots, Call Graph Collection, Exit Calls, and Development-Mode Performance Safeguards.
NEW QUESTION # 118
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: C
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 # 119
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