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Question 261

A tester asks GenAI to generate test cases for a feature, but the prompt does not identify the intended user roles. What is a likely consequence?

1. The model may generate scenarios that do not reflect role-specific behavior
2. The model will automatically determine all correct permissions
3. The model will guarantee complete authorization coverage
4. The generated cases will always be independent of user roles

Correct Answer: 1

Explanation

User roles can significantly affect application behavior, especially when authorization and permissions are involved. If the prompt does not identify relevant roles, GenAI may generate generic scenarios that overlook differences between administrators, standard users, managers, or other account types. This can result in missing authorization scenarios or inappropriate expected results. Providing role information gives the model better context for generating relevant tests. However, the generated cases should still be checked against the approved access-control requirements. GenAI should not be expected to infer every role or permission accurately without supporting information. Clear context improves relevance and reduces unsupported assumptions.

Question 262

Which technique can help a tester obtain more consistent GenAI-generated test cases across repeated requests?

1. Remove all constraints from the prompt
2. Use a clearly defined structure and provide representative examples
3. Ask for completely random test cases
4. Change the testing objective for every request

Correct Answer: 2

Explanation

Consistent prompting can help produce more predictable and comparable GenAI output. A clearly defined structure tells the model which fields and format should be used, while representative examples demonstrate the expected level of detail and terminology. This can reduce unnecessary variation between generated test cases. Random requests and changing objectives work against consistency, while removing constraints gives the model greater freedom to vary its responses. Consistency does not mean that the model will always produce identical output because generative systems can be probabilistic. Generated cases should still be reviewed for correctness, completeness, and alignment with the approved requirements.

Question 263

A tester uses GenAI to generate tests from a requirements document containing several undefined business terms. What should the tester do?

1. Ask GenAI to invent definitions for all undefined terms
2. Ignore the undefined terms
3. Clarify the terminology with an authoritative source before finalizing the tests
4. Replace every business term with a technical term

Correct Answer: 3

Explanation

Undefined business terminology can lead to different interpretations and consequently incorrect test conditions. The tester should seek clarification from an approved glossary, requirements owner, product owner, or other authoritative source. GenAI can identify potentially ambiguous terminology and suggest clarification questions, but it should not invent definitions that may conflict with actual business meaning. Replacing terms with technical alternatives can also change the intended meaning. Once the terminology has been clarified, the tester can provide the information to GenAI and regenerate or refine the relevant tests. This approach improves consistency, traceability, and alignment with the actual business requirements.

Question 264

A tester asks GenAI to create test cases using a document that contains confidential customer records. What should be considered before providing the document to the model?

1. Whether the document is long enough
2. Whether the generated response will contain enough test cases
3. Whether the document is classified as sensitive and whether the GenAI service is authorized to process it
4. Whether the model prefers PDF files

Correct Answer: 3

Explanation

Confidential customer records require careful handling before being provided to any GenAI service. The tester should determine the data classification, applicable privacy requirements, organizational policies, and whether the specific GenAI service is approved to process such information. File format or document length does not address the primary security concern. Where external processing is not authorized, alternatives such as anonymization, redaction, synthetic data, or an approved internal model may be appropriate. The tester should avoid assuming that a useful testing purpose automatically permits sharing sensitive information. Data protection requirements remain applicable when GenAI is incorporated into testing workflows.

Question 265

A tester wants GenAI to generate test scenarios that include both valid and invalid input combinations. Which prompt instruction is most useful?

1. Request coverage of positive, negative, boundary, and invalid combinations
2. Ask only for the most common successful workflow
3. Ask the model to avoid unusual inputs
4. Request only happy-path scenarios

Correct Answer: 1

Explanation

Explicitly requesting positive and negative conditions helps direct GenAI toward broader scenario coverage. Boundary values and invalid combinations are particularly useful for identifying behavior at limits and under erroneous conditions. If the prompt focuses only on common successful workflows, the generated output may omit important failure scenarios. Avoiding unusual inputs can also reduce coverage of edge cases. The tester should still compare the generated scenarios against requirements and risk-based testing objectives to determine whether the selected combinations are meaningful. Clear prompting can improve the breadth of generated ideas, but it does not guarantee complete coverage of every possible condition.

Question 266

A GenAI tool generates a test that expects a feature not mentioned in the approved requirements. What is the best response?

1. Treat the feature as an upcoming requirement
2. Include the test because the feature seems useful
3. Verify the feature with an authoritative source before using the test
4. Modify the requirements to match the generated test

Correct Answer: 3

Explanation

A generated expectation about an undocumented feature may be an unsupported assumption or hallucination. The tester should verify the feature against authoritative requirements, product documentation, or an appropriate stakeholder before using the generated test. It is inappropriate to modify approved requirements simply to accommodate GenAI output. Similarly, usefulness alone does not establish that a feature exists or belongs within the current testing scope. If the feature is confirmed, the test can be updated accordingly. If it is not supported, the generated test should be rejected or revised. This preserves traceability and prevents generated assumptions from becoming unofficial requirements.

Question 267

Which activity is most suitable for GenAI assistance during test documentation?

1. Making final release decisions independently
2. Drafting a test summary from verified execution information
3. Approving production deployment without review
4. Determining organizational security policy

Correct Answer: 2

Explanation

GenAI can be useful for drafting test summaries when it receives accurate and verified execution information. It can organize results, highlight notable findings, and improve readability for different audiences. However, the underlying execution data must come from authoritative sources and the resulting summary should be reviewed before publication. Release decisions, security policies, and other organizational decisions require appropriate human authority and governance. GenAI can support communication and documentation without replacing accountability. This distinction allows teams to benefit from automation while ensuring that important decisions and factual claims remain under appropriate human control.

Question 268

A tester asks GenAI to generate test automation code using a framework that has recently changed its API. What should the tester verify?

1. That the generated code matches the currently supported framework version
2. That the code uses as many methods as possible
3. That the model’s response is longer than the previous version
4. That old documentation is preferred over current documentation

Correct Answer: 1

Explanation

Framework APIs can change between versions, causing generated code based on outdated information to fail or behave incorrectly. The tester should verify the generated code against current, authoritative framework documentation and the version actually used by the project. Compilation or syntax correctness alone may not prove that the code uses supported APIs or follows current best practices. Current version information should be included in the prompt where relevant, and generated code should be executed and reviewed. This is especially important when GenAI’s underlying knowledge may not reflect recent framework releases or project-specific configuration.

Question 269

A team wants to maintain an audit trail for important GenAI-assisted testing activities. Which information can be useful to retain?

1. Only the final test case
2. Only the model’s name
3. Relevant prompts, generated outputs, model information, and human review records
4. Only the tester’s login password

Correct Answer: 3

Explanation

An audit trail can help a team understand how important generated artifacts were produced and reviewed. Depending on organizational policy, useful information may include the prompt, relevant source context, model or service version, generated output, revisions, validation results, and human approval records. Retaining such information can support reproducibility, troubleshooting, governance, and accountability. The exact information required depends on the organization’s risk and compliance requirements. Sensitive credentials such as passwords should never be stored as part of an audit trail. Proper records allow teams to understand the role of GenAI in important testing decisions without exposing unrelated confidential information.

Question 270

A tester receives two GenAI-generated test cases that use different wording but verify the same business rule. What should the tester consider?

1. Whether both cases provide distinct testing value or are redundant
2. Whether both cases have different sentence lengths
3. Whether the model generated them at different times
4. Whether both cases contain technical terminology

Correct Answer: 1

Explanation

Different wording does not necessarily mean different testing value. The tester should compare the objectives, inputs, conditions, expected results, and coverage of both cases. If they verify the same behavior under equivalent conditions, keeping both may introduce unnecessary redundancy. If they exercise meaningfully different conditions or risks, both may be justified. This evaluation helps maintain a focused and maintainable test suite. GenAI can generate multiple variations of similar scenarios, so duplicate detection and human review are important parts of GenAI-assisted test design. The objective is not simply to maximize the number of generated cases but to retain useful and distinct coverage.

Question 271

Which statement best describes the role of human oversight when GenAI is used for software testing?

1. Human oversight is unnecessary once a model has been validated
2. Human oversight is mainly required for formatting
3. Human oversight helps verify generated content and supports accountability
4. Human oversight should only occur after production deployment

Correct Answer: 3

Explanation

Human oversight provides an important control over the risks associated with GenAI-generated testing artifacts. Testers can verify requirements, expected results, data, security implications, completeness, and other quality characteristics that a model cannot reliably guarantee. Human review also establishes accountability for decisions about whether generated content should be accepted or used. Model validation can reduce some risks but does not eliminate the need for review because project context and requirements change. Oversight should occur at appropriate points before generated artifacts are relied upon. This approach treats GenAI as an assisting technology while retaining professional responsibility within the testing team.

Question 272

A tester wants to use GenAI to identify missing test scenarios in an existing regression suite. What information would be most useful to provide?

1. Only the number of existing test cases
2. The relevant requirements, current regression tests, and known coverage information
3. Only the project name
4. A request to invent missing functionality

Correct Answer: 2

Explanation

Identifying gaps requires enough information to compare intended behavior with existing coverage. Relevant requirements define what should be tested, while current regression cases show what is already covered. Coverage information can further help identify areas that may need additional attention. Providing only a test count or project name gives insufficient context for meaningful analysis. Asking the model to invent functionality can introduce unsupported assumptions. GenAI can assist with comparing artifacts and suggesting potential gaps, but testers should validate every suggested gap against authoritative requirements and risk information. This makes the process more systematic while avoiding false conclusions about missing functionality.

Question 273

A tester asks GenAI to create a test plan and provides project scope, risks, schedule constraints, and test objectives. Why is this context useful?

1. It helps the model align the draft with the project’s actual testing conditions
2. It guarantees the final test plan will be approved
3. It removes the need for risk analysis
4. It ensures that no test activity will be missed

Correct Answer: 1

Explanation

Project-specific context helps GenAI produce a test-plan draft that is more relevant to the actual testing situation. Scope defines what is included, risks help identify priorities, schedule constraints affect planning, and objectives clarify the intended outcomes. Without this context, the generated plan may contain generic activities that do not fit the project. However, providing detailed information does not guarantee completeness or approval. Test professionals and stakeholders must review the draft, verify its assumptions, and adjust it according to project governance. GenAI can accelerate planning documentation, but it does not replace professional risk assessment or stakeholder decision-making.

Question 274

A GenAI-generated test script contains a hard-coded password copied from an example in the prompt. What should the tester do?

1. Keep the password because it was generated automatically
2. Replace or remove the sensitive credential and review the script for other security issues
3. Publish the script without changes
4. Store the password in additional scripts

Correct Answer: 2

Explanation

Credentials should not be embedded unnecessarily in test scripts, especially when they originate from sensitive examples. The tester should remove or replace the credential with an approved mechanism such as secure test secrets, environment variables, or a controlled credential store. The script should also be reviewed for other sensitive information that may have been reproduced by GenAI. This example demonstrates how generated code can unintentionally propagate insecure practices from its context. Security review remains necessary even when the generated script appears technically functional. GenAI-assisted automation should follow the same credential-management and security standards applied to manually developed testing code.

Question 275

Which factor should a tester consider when deciding whether a GenAI-generated output is relevant to the requested testing task?

1. Whether the output directly supports the defined test objective
2. Whether the output contains advanced terminology
3. Whether the output is unusually long
4. Whether the output uses persuasive language

Correct Answer: 1

Explanation

Relevance is determined by how directly the generated output supports the defined testing objective. A test case may be technically detailed and well written while still being irrelevant to the feature or risk under investigation. The tester should therefore compare generated scenarios with the stated objective, scope, requirements, and relevant risks. Length and terminology do not demonstrate usefulness. Persuasive language can also make an incorrect response appear convincing. Evaluating relevance helps teams avoid spending time reviewing or maintaining generated artifacts that do not contribute meaningful testing value. This is especially important when GenAI produces large quantities of possible scenarios.

Question 276

A tester uses GenAI to generate SQL statements for database testing. What should be done before executing the generated statements against a shared environment?

1. Execute them immediately because generated SQL is safe by default
2. Review the statements for correctness, unintended changes, and security risks
3. Remove all WHERE clauses
4. Grant the model administrative database privileges

Correct Answer: 2

Explanation

Generated SQL should be treated as untrusted code until it has been reviewed. The tester should verify syntax, target tables, filters, joins, expected results, and whether the statement could modify or delete unintended data. Security considerations should also be evaluated, particularly when generated statements involve authentication, permissions, or user-supplied input. Running unreviewed SQL against a shared environment could cause data corruption or other operational problems. Appropriate permissions and controlled test environments should be used. GenAI can accelerate query creation, but the tester remains responsible for validating generated database operations before execution.

Question 277

A tester asks GenAI to summarize a long defect report. Which approach can improve the reliability of the summary?

1. Ask the model to invent missing evidence
2. Provide the relevant defect information and require important facts to be preserved
3. Remove the actual failure details
4. Ask for an opinion about the developer

Correct Answer: 2

Explanation

A useful defect summary should preserve the important factual information from the original report. Relevant symptoms, reproduction steps, expected and actual results, environment details, and known evidence can help GenAI create a concise but accurate summary. Asking the model to invent missing evidence introduces hallucination risk, while removing failure details can make the summary misleading. Personal opinions about developers are also unrelated to factual defect communication. After generation, the tester should compare the summary with the original report to confirm that critical information has not been omitted or changed. GenAI can improve readability while the source evidence remains authoritative.

Question 278

A tester wants to assess whether a GenAI-generated test suite is sufficiently diverse. Which question is most relevant?

1. Does the suite contain many test cases regardless of their similarity?
2. Does the suite cover meaningful variations in inputs, conditions, workflows, and risks?
3. Are all test cases written in identical sentences?
4. Did the model generate the suite quickly?

Correct Answer: 2

Explanation

Test diversity is concerned with meaningful variation rather than simply increasing the number of test cases. A diverse suite may exercise different inputs, boundary conditions, user roles, workflows, error conditions, integrations, and risk areas. Repeated cases with minor wording changes may add little value. The tester should therefore evaluate whether the suite covers distinct and relevant conditions connected to the application’s requirements and risks. Generation speed is useful for productivity but does not establish diversity. Human review, coverage analysis, and comparison with existing tests can help determine whether GenAI has produced genuinely useful variations rather than a large collection of near-duplicates.

Question 279

A tester notices that GenAI consistently generates examples reflecting only one type of user demographic. What should the tester consider?

1. Potential bias in the generated data or examples
2. That the model has proven all users are represented equally
3. That demographic variation is irrelevant in every application
4. That more identical examples should be generated

Correct Answer: 1

Explanation

Repeatedly generating examples that represent only one demographic may indicate bias in the model, training patterns, examples, or prompting context. The tester should examine whether the limited representation could affect test coverage or product behavior. Where demographic diversity is relevant, the test data should include appropriate and justified variations based on requirements and risk. This does not mean that every application requires every possible demographic category, but the team should avoid unintentionally excluding relevant users. GenAI output should therefore be reviewed for potential bias rather than automatically treated as representative. Appropriate domain expertise and requirements should guide decisions about meaningful test-data diversity.

Question 280

A testing organization introduces GenAI for test design and requires testers to document when generated content is accepted after review. What is a key benefit of this practice?

1. It makes all generated content automatically correct
2. It prevents GenAI from producing future errors
3. It provides evidence of human review and supports accountability
4. It eliminates the need for testing standards

Correct Answer: 3

Explanation

Documenting acceptance decisions provides evidence that generated content was reviewed before being used in the testing process. This can support accountability, governance, auditability, and later investigation if questions arise about how an artifact was created or approved. The practice does not make GenAI output automatically correct or prevent future errors. Testing standards and other controls remain necessary. A documented review process can also help organizations identify recurring problems in generated content and improve prompts, validation procedures, or usage policies over time. This creates a controlled workflow in which GenAI contributes to productivity while human professionals retain responsibility for the final testing artifacts.