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Question 381

A tester asks GenAI to generate tests for a feature whose behavior depends on several configurable system settings. What should be included in the prompt?

1. Only the feature name
2. The tester’s preferred test-case format
3. The number of existing defects
4. Relevant configuration values, dependencies, and expected behavior

Correct Answer: 4

Explanation

Configuration settings can significantly change how a feature behaves. When generating tests, the tester should provide relevant configuration values, dependencies, allowed combinations, and expected behavior. This helps GenAI identify scenarios that might otherwise be overlooked. The tester should also distinguish between supported configurations and invalid combinations. Generated cases should be reviewed against the actual configuration specification because GenAI may invent unsupported settings or interactions. Providing targeted configuration context can improve test coverage while reducing irrelevant scenarios. The final test suite should reflect approved system behavior rather than assumptions made by the model.

Question 382

A GenAI-generated test case contains an expected result that conflicts with an approved acceptance criterion. What should the tester do?

1. Use the approved acceptance criterion as the authoritative reference
2. Use whichever result sounds more realistic
3. Keep both results as equally valid
4. Ask the model to decide which requirement is correct

Correct Answer: 1

Explanation

Approved acceptance criteria are authoritative for determining intended behavior. If a generated expected result conflicts with them, the tester should correct or reject the generated result rather than treating both as equally valid. GenAI may interpret requirements incorrectly or generate plausible but unsupported behavior. The conflict should also be investigated if it indicates that another specification is inconsistent. Keeping incorrect expectations can result in false failures or missed defects. GenAI can assist in identifying discrepancies, but approved project documentation and authorized stakeholders should determine the expected behavior.

Question 383

A tester wants GenAI to generate tests for an application that supports different time zones. Which information is most important?

1. The application’s logo
2. The number of developers
3. Supported time zones, date-time rules, and expected behavior
4. The length of the user manual

Correct Answer: 3

Explanation

Time-zone behavior can affect dates, timestamps, scheduling, notifications, reporting, and calculations. GenAI needs relevant information about supported time zones, daylight-saving behavior where applicable, date-time formats, conversion rules, and expected results. Without this context, generated tests may overlook important boundary conditions or assume incorrect behavior. Testers should also consider transitions around midnight and other date boundaries where relevant. Generated cases must be checked against the application’s approved requirements. Providing precise temporal rules enables GenAI to suggest meaningful scenarios rather than relying on generic assumptions about how date and time functionality should work.

Question 384

A tester wants to reduce unsupported assumptions in GenAI-generated test cases. Which prompt approach is most useful?

1. Ask for as many assumptions as possible
2. Instruct the model to use only the supplied requirements and explicitly identify missing information
3. Remove all requirements from the prompt
4. Ask the model to invent reasonable business rules

Correct Answer: 2

Explanation

Explicitly restricting the source information can reduce unsupported assumptions. The prompt can instruct GenAI to base test cases only on the supplied requirements and to clearly identify information that is missing rather than inventing it. This makes gaps visible to the tester and provides opportunities for clarification. It does not guarantee that hallucinations will disappear, so generated content still requires review. Asking the model to invent business rules can introduce behavior that has never been approved. Clear source boundaries therefore help maintain traceability and improve the reliability of GenAI-assisted test generation.

Question 385

A tester asks GenAI to generate test scenarios for a search function. Which set of conditions provides useful coverage?

1. Valid searches, empty input, special characters, no results, and boundary conditions
2. Only one common search term
3. Only searches that return results
4. Only the application’s home page

Correct Answer: 1

Explanation

Search functionality can fail under several types of conditions. Useful scenarios can include normal valid searches, empty input, special characters, unsupported characters, very long queries, no-result conditions, and other applicable boundaries. The exact scenarios should be based on the search requirements and supported input rules. Testing only successful common searches may miss important validation and error-handling defects. GenAI can help enumerate potential conditions, but testers should verify that each scenario is relevant to the actual application. A balanced set of positive, negative, and boundary conditions generally provides more meaningful coverage than repeatedly testing typical searches.

Question 386

A testing team uses a GenAI service to analyze proprietary source code. Which action should occur before the code is submitted?

1. Remove all comments from the code
2. Convert the code to another programming language
3. Increase the prompt length
4. Confirm that the service is approved to process proprietary code

Correct Answer: 4

Explanation

Proprietary source code can contain valuable intellectual property and confidential implementation details. Before submitting it to a GenAI service, the team should confirm that the service is authorized for such information and that appropriate contractual, security, retention, and access controls are in place. If the service is not approved, the team may need to use an authorized environment or provide appropriately sanitized information. Changing the programming language or removing comments does not automatically eliminate confidentiality concerns. Proper data governance is therefore an important prerequisite for GenAI-assisted source-code analysis.

Question 387

A tester asks GenAI to generate tests for a notification system. Which condition should be included when the requirement states that notifications must not be sent to disabled accounts?

1. Sending notifications only to active accounts
2. Verifying that disabled accounts do not receive notifications
3. Changing the notification color
4. Increasing the notification message length

Correct Answer: 2

Explanation

The requirement explicitly defines behavior for disabled accounts, so a negative test should verify that notifications are not sent to them. This scenario checks whether the system correctly enforces the stated business rule. A positive test for active accounts may also be appropriate, but it does not replace the negative condition. GenAI can help identify both types of scenarios when the requirement is supplied clearly. The tester should verify the expected result against the approved requirement and ensure that test data can distinguish active and disabled account states.

Question 388

A tester is evaluating GenAI-generated test cases and finds that several cases are technically correct but unrelated to the current feature. Which quality attribute is primarily affected?

1. Reproducibility
2. Formatting
3. Relevance
4. Execution speed

Correct Answer: 3

Explanation

Relevance concerns whether generated content is appropriate to the requested testing objective and system context. A technically correct test can still have little value if it concerns unrelated functionality. Testers should therefore evaluate generated cases not only for correctness but also for applicability to the feature, requirements, risks, and defined scope. Irrelevant cases can increase maintenance effort and obscure important scenarios. Providing focused context and explicit scope in the prompt can help reduce irrelevant output. Human review remains necessary because GenAI may generate plausible scenarios that are technically valid but outside the intended testing scope.

Question 389

A tester asks GenAI to generate tests for a data-import function. Which scenario is particularly useful as a negative test?

1. Importing a correctly formatted valid file
2. Importing the smallest supported valid file
3. Viewing the import history
4. Importing a file with invalid or malformed data

Correct Answer: 4

Explanation

Negative testing checks how the system handles invalid or unexpected conditions. A malformed import file can reveal weaknesses in validation, error handling, transaction management, and user feedback. Other useful negative scenarios may include missing mandatory fields, invalid data types, duplicate records, unexpected encodings, or excessive file sizes, depending on the requirements. Valid imports remain important for positive testing but do not replace negative scenarios. GenAI can help enumerate possible invalid conditions, while testers should verify that each condition is supported by the actual import specification and that the expected behavior is clearly defined.

Question 390

A tester wants GenAI to generate tests for a subscription system with monthly and annual plans. What should the prompt include?

1. Only the subscription screen name
2. Plan types, pricing rules, renewal behavior, and applicable business conditions
3. The number of developers
4. The application’s color scheme

Correct Answer: 2

Explanation

Subscription testing depends heavily on business rules. The prompt should provide information about plan types, prices, billing cycles, renewal behavior, cancellation rules, upgrades, downgrades, discounts, and other applicable conditions. This context allows GenAI to generate more meaningful combinations and edge cases. Without business rules, the model may invent unsupported pricing or renewal behavior. Generated tests should be compared with the approved subscription specification before execution. Providing detailed business context helps improve coverage of financial and lifecycle scenarios while keeping the generated expectations aligned with actual product behavior.

Question 391

A tester wants to use GenAI to improve an existing test script without changing its intended behavior. What should the prompt emphasize?

1. Preserve the existing functionality while improving the requested aspects
2. Rewrite the entire script without constraints
3. Add new functionality automatically
4. Remove all existing assertions

Correct Answer: 1

Explanation

When modifying existing automation, preserving intended behavior is essential unless a functional change is explicitly requested. The prompt should identify what may be changed, such as readability, duplication, maintainability, or performance, while instructing GenAI to preserve existing test objectives and assertions. The generated code should then be compared with the original to ensure that important behavior has not been lost. Automatic addition of new functionality can introduce unintended changes. Human code review and execution of appropriate regression tests remain necessary after AI-assisted refactoring.

Question 392

A tester asks GenAI to generate tests for a requirement that contains the phrase “as soon as possible.” What should the tester do?

1. Assume it means one second
2. Ask GenAI to select a reasonable value
3. Obtain a measurable and agreed definition of the timing requirement
4. Remove timing tests entirely

Correct Answer: 3

Explanation

“As soon as possible” is ambiguous and does not provide a measurable test condition. A tester should obtain clarification from the appropriate stakeholder and establish a measurable expectation, such as a response-time threshold or processing deadline. GenAI can help identify the ambiguity and suggest questions, but it should not define the official requirement. Once a measurable condition is available, GenAI can help generate suitable positive, negative, and boundary scenarios. Clarifying vague requirements before test generation reduces the risk of creating tests based on arbitrary assumptions and improves the overall testability of the requirement.

Question 393

A tester wants to determine whether a GenAI-generated test suite contains sufficient negative testing. Which approach is appropriate?

1. Count only the total number of test cases
2. Review whether relevant invalid, exceptional, and prohibited conditions are covered
3. Remove all positive tests
4. Accept the suite because it was generated from requirements

Correct Answer: 2

Explanation

Negative-test coverage should be evaluated by examining whether relevant invalid, exceptional, prohibited, and unexpected conditions are represented. Simply counting test cases does not reveal whether important negative conditions are missing. The tester should review the requirements, business rules, security expectations, and error-handling behavior to identify applicable negative scenarios. Positive testing remains necessary and should not be removed. GenAI can help suggest invalid conditions, but its output may overlook important exceptions. A structured review against requirements and risks provides stronger evidence that the generated suite includes appropriate negative testing.

Question 394

A tester receives a GenAI-generated test summary that claims a defect was fixed, but the execution results do not confirm the fix. What should the tester do?

1. Publish the claim because the model generated it
2. Change the execution results to match the summary
3. Ignore the discrepancy
4. Correct the summary based on verified execution evidence

Correct Answer: 4

Explanation

A claim about a defect being fixed should be supported by actual verification results. If execution evidence does not confirm the fix, the generated summary should be corrected rather than treated as authoritative. The tester should investigate the discrepancy and determine the actual status based on reliable test results and defect records. GenAI can unintentionally convert tentative or incomplete information into a confident statement. Human review is therefore particularly important for status reporting. Formal reports should reflect verified evidence, not assumptions contained in generated summaries.

Question 395

A tester asks GenAI to generate test cases using examples of previously approved tests. What is a potential benefit of this approach?

1. It guarantees that every new test will be correct
2. It eliminates the need for requirements
3. It can help the model follow the team’s terminology and preferred test structure
4. It guarantees identical output every time

Correct Answer: 3

Explanation

Providing representative approved examples can help GenAI understand the team’s terminology, organization, level of detail, and preferred test structure. This is commonly associated with few-shot prompting. The examples provide guidance about the desired format and style, but they do not guarantee correctness or complete coverage. Current requirements should still be supplied when they are necessary to define the behavior being tested. Testers should also ensure that examples do not contain inappropriate sensitive information. Generated cases require review because the model may copy assumptions from examples that do not apply to the new feature.

Question 396

A tester is using GenAI to create a test-data set for an account-management feature. Which practice helps avoid exposing personal information?

1. Use appropriately synthetic or anonymized data instead of unnecessary real customer data
2. Copy the entire production customer database
3. Include real passwords for realism
4. Upload personal records to any available AI service

Correct Answer: 1

Explanation

Synthetic or appropriately anonymized data can reduce the risk associated with exposing real personal information during test-data generation. Testers should use only the information necessary for the testing objective and follow applicable privacy and organizational requirements. Copying production databases or including real passwords creates unnecessary security and privacy risks. The selected GenAI service should also be approved for the type of data being processed. Synthetic data should still be validated because it needs to satisfy application rules, relationships, formats, and boundary conditions to be useful for testing.

Question 397

A tester asks GenAI to identify potential gaps in tests for a password-management feature. Which area should be considered?

1. Only the visual design of the password page
2. Only successful password changes
3. Only the application’s help documentation
4. Password policy, invalid attempts, reset behavior, expiration, and security controls

Correct Answer: 4

Explanation

Password-management functionality can involve multiple security and functional conditions. Relevant tests may include password-policy enforcement, invalid attempts, reset flows, expiration, reuse restrictions, account recovery, session behavior, and appropriate security controls. The exact scenarios depend on the application’s approved requirements. Focusing only on successful password changes may miss important weaknesses. GenAI can help identify potential gaps when the relevant requirements are provided, but testers should verify the suggestions against the security specification and authorized testing scope. Security-sensitive scenarios should receive appropriate review before execution.

Question 398

A team wants to make GenAI-assisted testing easier to audit. Which information should be retained for important generated artifacts?

1. Only the final generated text
2. Relevant prompt, source context, tool/model information, output, and review decisions
3. Only the tester’s name
4. Only the date the artifact was created

Correct Answer: 2

Explanation

Auditability improves when the team can understand how an important generated artifact was produced and reviewed. Depending on organizational requirements, useful records can include the prompt, relevant source material or references, model or tool information, generated output, modifications, reviewer identity, and approval decisions. Retaining only the final text may make it difficult to reconstruct the process or investigate discrepancies. The exact retention requirements depend on the project’s governance and risk level. Maintaining appropriate records supports traceability, accountability, reproducibility, and continuous improvement of GenAI-assisted testing activities.

Question 399

A tester asks GenAI to identify duplicate tests in a large regression suite. What should the tester verify before deleting any suggested duplicates?

1. Whether the tests truly provide equivalent coverage and have no meaningful differences
2. Whether the test descriptions use identical wording
3. Whether the model generated both tests in the same response
4. Whether the tests have the same author

Correct Answer: 1

Explanation

Two tests can have different wording while covering the same behavior, or similar wording while exercising different conditions. Therefore, deletion decisions should consider inputs, preconditions, actions, expected results, data, risks, and coverage. A GenAI suggestion can help identify possible duplicates, but it should not automatically trigger deletion. The tester should confirm that consolidating the cases will not remove meaningful coverage or traceability. This is particularly important in large regression suites where apparently similar tests may exist for different configurations or risks. Human review protects against unintended loss of useful coverage.

Question 400

A testing organization is defining its overall approach to GenAI. Which principle is most appropriate?

1. Use GenAI according to defined risks, controls, governance, and appropriate human oversight
2. Allow GenAI to make all testing decisions independently
3. Use GenAI for every testing activity regardless of risk
4. Avoid validating generated content because validation reduces productivity

Correct Answer: 1

Explanation

A responsible GenAI approach should consider the purpose, risks, data sensitivity, quality requirements, and appropriate level of human oversight for each testing activity. GenAI can provide significant assistance with analysis, test generation, automation, documentation, and exploratory work, but generated content may contain errors, omissions, outdated information, or unsupported assumptions. Organizations should establish suitable governance, approved tools, data-handling rules, review practices, and evaluation criteria. Higher-risk activities generally require stronger controls. The goal is not to use GenAI everywhere or avoid it entirely, but to apply it in a controlled manner that supports testing objectives while maintaining accountability for important decisions.