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NEW QUESTION # 86
Which type of Data Collector will capture code data such as method arguments, variables, and return values?
Answer: B
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 # 87
An AppDynamics deployment has Business Transaction Lock Down turned on. The company has just added an important service to its application and wants to track this service as a unique Business Transaction. What action is needed to achieve this?
Answer: D
Explanation:
When Business Transaction Lock Down is enabled in AppDynamics, no new business transactions will be automatically discovered to avoid uncontrolled growth in the number of business transactions. To track a new service as a unique Business Transaction, one needs to create a Custom Match Rule that specifies the criteria for identifying the business transaction. In this case, the Custom Match Rule should be based on the Web Service Name and Operation Name which are the distinguishing characteristics of the new service. This allows for the precise identification and monitoring of the service within the AppDynamics platform.References:AppDynamics documentation on Business Transaction configuration and Custom Match Rules.
NEW QUESTION # 88
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 # 89
What are two differences between creating a Transaction Group using the 'Create Group' action and defining a Transaction Detection rule? (Choose two.)
Answer: B,D
Explanation:
Creating a Transaction Group using the 'Create Group' action in AppDynamics allows for the aggregation of data from multiple transactions under a single group, facilitating a consolidated view of similar transactions.
This differs from defining a Transaction Detection Rule, which typically focuses on identifying and categorizing individual transactions based on specific criteria. Transaction Groups do not change the names of incoming requests nor reduce the number of overall business transactions; instead, they provide a method for organizing and analyzing related transactions collectively, offering a streamlined approach compared to individually configuring Transaction Detection Rules for each transaction.
References:
AppDynamics documentation on Business Transactions: Covers the concept of Transaction Groups and their role in organizing and analyzing transaction data, as well as the process for creating and managing Transaction Detection Rules.
NEW QUESTION # 90
On a snapshot flow map, which three metrics indicate why the snapshot might have been taken? (Choose three.)
Answer: A,B,D
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 # 91
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