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| Section | Objectives |
|---|---|
| Gosu Programming | - Core Gosu syntax and constructs - Business logic implementation in Guidewire |
| Data Model and Configuration | - Typelist configuration and metadata - Entity model and extensions |
| Deployment and Environment Management | - Deployment lifecycle and best practices - Environment configuration |
| InsuranceSuite Architecture | - PolicyCenter, BillingCenter, ClaimCenter interaction - Data flow and system integration concepts |
| Testing and Debugging | - Unit testing in Guidewire environment - Debugging tools and techniques |
| User Interface (PCF) | - Page Configuration Files (PCF) structure - UI customization and navigation flows |
| Guidewire Platform Fundamentals | - Platform architecture basics - InsuranceSuite product overview |
| Integration and APIs | - Inbound and outbound integration mechanisms - Web services and integration patterns |
| Business Rules and Logic | - Rule execution order and lifecycle - Validation rules and workflows |
>> Guidewire InsuranceSuite-Developer Simulationsfragen <<
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120. Frage
An insurer ran the DBCC checks against a copy of their PolicyCenter production database in a non-production environment to check for errors before promoting the code to production. Three errors with high counts were found in the category " Data update and reconciliation. " What are two best practices for resolving the errors?
(Select two)
Antwort: B,D
Begründung:
Database Consistency Checks (DBCC) are a critical part of the System Health and Quality framework. When these checks return errors, especially in categories like " Data update and reconciliation, " it indicates a mismatch between the database ' s physical state and the application's metadata expectations.
According to Guidewire best practices, the first essential step (Option D) is root cause analysis. A developer must determine why the data is inconsistent. For example, if a high count of errors appears after a code change, it likely means a Gosu rule, an integration, or a newly added required field is not being populated correctly. Fixing the symptom with a SQL script without addressing the code responsible will only lead to the errors recurring. Addressing the logic ensures that the " faucet " of bad data is turned off before attempting to
" mop up " the existing data.
The second best practice (Option E) is leveraging the Guidewire Community and Knowledge Base. Many DBCC errors, particularly those related to base application upgrades or standard entities, are well- documented. Guidewire provides specific remediation scripts or configuration adjustments for known issues.
Options A, B, and C are strictly prohibited under Guidewire SurePath standards. Running SQL scripts " immediately " without analysis can lead to further corruption. Waiting for error counts to increase (Option B) ignores potential system instability. Promoting to production (Option C) while knowing errors exist is a violation of deployment quality gates and can lead to production downtime. By combining technical analysis with community resources, developers ensure a stable transition to the production environment.
121. Frage
What is a commit in Git?
Antwort: A
122. Frage
What are two types of Guidewire Profiler? (Select two)
Antwort: B,E
Begründung:
The Guidewire Profiler is the primary tool used by developers to diagnose performance issues in InsuranceSuite. It functions by instrumenting " stacks " of code execution and reporting the time spent in various operations like database queries or Gosu rules. According to the System Health and Quality documentation, the Profiler is categorized by how it is triggered and what it monitors.
The Web Profiler (Option A) is used to analyze performance within the user interface. When enabled, it captures the execution of a single web request (a page load or a button click), allowing the developer to see exactly which PCF widgets or page-level Gosu expressions are causing latency.
The Entry-point Profiler (Option D) is a broader category that includes profiling for non-UI operations. Entry points are the " doors " through which the application begins a task. Common entry points that can be profiled include Batch Processes, Work Queue Executors, and Web Services (API calls). By selecting an entry-point profiler, a developer can monitor the performance of background jobs or external integrations that do not have a direct UI component.
Options like Database Performance (Option B) are not specific types of the Profiler itself, but rather outcomes or areas of analysis that the profiler provides. Exit-point (Option C) and Worksheet (Option E) are not recognized classifications of the profiling tool in the Guidewire developer curriculum. Understanding these two types allows developers to strategically apply performance monitoring across the entire application stack.
123. Frage
This sample code uses array expansion with dot notation and has performance issues:
What best practice is recommended to resolve the performance issues?
Antwort: B
Begründung:
In the Guidewire InsuranceSuite Developer training, specifically within the Advanced Gosu modules, the " Array Expansion Operator " (*.) is identified as a double-edged sword. While it provides a clean, declarative syntax for gathering properties from an array of objects into a new collection, it is a common source of performance degradation in complex configurations.
The technical reason for this performance hit is that every time the expansion operator is invoked, Gosu must create an intermediate, temporary collection in memory to hold the projected values. If you are expanding multiple levels (e.g., Claim.Exposures*.Contacts*.Address), the system is essentially building multiple " throwaway " lists in the application server ' s heap. For large datasets, this leads to high memory overhead and triggers frequent garbage collection cycles, which slows down the entire application.
Guidewire's official recommendation is to rewrite the code using a nested for loop (Option A). By using explicit procedural iteration, the developer eliminates the need for these hidden intermediate collections. A nested loop allows for " streaming " the data-processing each item as it is reached rather than collecting everything into a list first. This is significantly more memory-efficient. Additionally, nested loops allow developers to integrate " early exit " logic or filters that can prevent the system from even attempting to load certain records from the database, further optimizing the transaction. Following this best practice ensures that the code is not only easier to debug using the Guidewire Profiler but also scales predictably as the insurer ' s data volume grows.
124. Frage
Database Consistency Checks classified as ' Other data integrity expectations ' in Guidewire Cloud must be addressed before deploying code into production in the Cloud. Which two are best practices for resolving these errors? (Select two)
Antwort: A,B
Begründung:
The Database Consistency Check (DBCC) tool is an essential component of the Guidewire Cloud Standards.
Errors flagged by this tool are categorized by severity and impact. While " Critical " errors (such as violated foreign keys or missing required fields) must always be resolved, the category ' Other data integrity expectations ' often contains legacy data issues or minor inconsistencies that may not necessarily block application functionality but indicate a deviation from best practices.
According to the System Health and Quality curriculum, the first best practice for handling a high volume of these errors is Prioritization (Option C). A developer should focus on errors that have a high " count " and are
" recent. " High-count errors indicate a systemic issue in the code (such as a misconfigured rule or integration) that is actively polluting the database. Recent errors suggest that the issue is current and likely caused by a recent change in the configuration. Addressing these first provides the highest return on investment for system stability.
The second best practice is collaboration with Guidewire Cloud Assurance (Option D). In the GWCP model, the Cloud Assurance team acts as the final gatekeeper for production deployments. Because some " Other " errors may be false positives or acceptable legacy artifacts, the developer must provide an analysis of the root cause and the potential impact. The Cloud Assurance team then makes the final determination on whether the error is a " blocker " for the production promotion. This collaborative approach ensures that the path to production is managed with professional oversight, adhering to the SurePath methodology.
125. Frage
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