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Question 81

Which characteristic of GenAI makes it useful for generating multiple variations of test scenarios?

1. It can only reproduce predefined test cases
2. It requires every scenario to be manually coded
3. It cannot process natural-language instructions
4. It can generate varied content based on patterns and provided context

Correct Answer: 4

Explanation

GenAI can generate multiple variations of test scenarios by using patterns learned during training together with the context and instructions supplied by the tester. For example, a tester can provide a requirement and ask for different combinations of valid, invalid, boundary, and exceptional conditions. The generated scenarios may help broaden the initial set of testing ideas. However, variation does not automatically mean completeness or correctness. Testers should review the generated scenarios to remove irrelevant or duplicate cases and verify that important requirements, risks, and business rules are adequately represented.

Question 82

What is an appropriate way to use GenAI when reviewing requirements for potential testability issues?

1. Allow the model to approve requirements automatically
2. Ask it to identify ambiguities, missing information, or unclear acceptance criteria for human review
3. Replace all requirements with generated text
4. Ignore domain-specific terminology

Correct Answer: 2

Explanation

GenAI can assist requirements review by highlighting potentially ambiguous wording, missing information, inconsistent terminology, or unclear acceptance criteria. These observations can provide useful questions for testers and analysts to investigate. However, the model’s interpretation may itself be incorrect, so identified issues should be reviewed by people who understand the business and technical context. GenAI should not automatically approve, reject, or rewrite requirements without appropriate governance. Its value in this situation is primarily to accelerate identification of possible testability concerns and provide additional perspectives for human analysis.

Question 83

Which information would provide the most useful context when asking GenAI to generate authorization test scenarios?

1. User roles, permissions, protected functions, and relevant authorization rules
2. The preferred color of the testing dashboard
3. The tester’s personal computer specifications
4. The number of characters allowed in the project name

Correct Answer: 1

Explanation

Authorization testing depends heavily on understanding which users or roles are permitted to perform particular actions. Providing user roles, permissions, protected functions, and authorization rules gives GenAI the context needed to suggest meaningful positive and negative scenarios. For example, the tester may want cases involving authorized users, unauthorized users, role changes, and attempts to access restricted functions. Other information that does not affect authorization behavior adds little value. Generated scenarios must still be checked against the actual security requirements because the model may misunderstand permissions or omit important combinations.

Question 84

What is one potential benefit of using GenAI to refactor existing test automation code?

1. It guarantees that refactored code has no defects
2. It eliminates the need for regression testing
3. It can suggest changes that improve readability or reduce repetitive code
4. It automatically understands every project-specific framework rule

Correct Answer: 3

Explanation

GenAI can help identify repetitive structures and suggest refactoring approaches that may improve readability, maintainability, or reuse in test automation code. For example, it may suggest extracting common steps into helper functions or simplifying repeated logic. However, generated refactoring suggestions can unintentionally alter behavior or conflict with project-specific conventions. Therefore, the code should be reviewed and regression-tested after changes are made. GenAI can assist with identifying and implementing possible improvements, but it does not guarantee that the resulting automation remains functionally equivalent or fully compatible with the existing framework.

Question 85

A tester asks GenAI to generate test cases for an application but provides only its name. What is the main limitation?

1. The model will always refuse the request
2. The model will automatically retrieve all private requirements
3. The model will execute the application before responding
4. The available context is insufficient for reliable application-specific test design

Correct Answer: 4

Explanation

Knowing only the application name generally provides very little information about its requirements, business rules, users, workflows, risks, and expected behavior. A GenAI model may still generate plausible generic test ideas, but those ideas may not reflect the actual application. More useful results can be obtained by supplying relevant requirements, acceptance criteria, user roles, workflows, constraints, and testing objectives. Even with additional context, generated test cases require validation. The example demonstrates why prompt context is important when using GenAI for application-specific testing activities.

Question 86

Which action is most appropriate when GenAI generates a test expected result that conflicts with the approved specification?

1. Compare the generated result with the approved specification and correct the test
2. Change the specification to match the generated result
3. Accept the generated result because it is detailed
4. Remove the requirement from the test documentation

Correct Answer: 1

Explanation

The approved specification should be treated as the authoritative source for expected system behavior unless an authorized change has been made. If GenAI generates an expected result that conflicts with the specification, the tester should investigate the discrepancy and correct or reject the generated test information. The model’s output should not override an approved requirement. Such conflicts can also reveal ambiguous or outdated documentation that needs clarification through the appropriate process. Human review ensures that generated testing artifacts remain aligned with the officially defined behavior of the system.

Question 87

Which practice can help a team evaluate whether GenAI is providing useful testing outputs over time?

1. Measuring only how many prompts testers submit
2. Defining evaluation criteria such as correctness, relevance, completeness, and consistency
3. Accepting every generated output
4. Measuring only the length of generated responses

Correct Answer: 2

Explanation

A team can evaluate GenAI effectiveness by defining clear quality criteria for its outputs. Depending on the testing activity, these may include correctness, relevance, completeness, consistency, traceability, and usefulness. For example, generated test cases can be sampled and reviewed against requirements to determine whether they identify meaningful scenarios. Measuring only response length or the number of prompts does not provide a meaningful assessment of quality. Regular evaluation can help the team identify weaknesses in prompts, workflows, model selection, or review procedures and make informed improvements.

Question 88

Which situation is an example of using GenAI for test result analysis?

1. Physically replacing a failed server
2. Installing a new network cable
3. Asking GenAI to summarize patterns in provided pass/fail results and defect information
4. Changing the production database schema

Correct Answer: 3

Explanation

GenAI can assist with test result analysis when it is given relevant execution results, defect information, logs, or other evidence. It may summarize patterns, identify frequently occurring failure descriptions, or organize large amounts of textual test information. Such analysis can help testers focus their investigation. However, the generated interpretation should be checked against the original evidence because the model may overlook important details or infer relationships that are not supported. GenAI therefore works well as an analytical aid while the tester remains responsible for validating conclusions and determining appropriate follow-up actions.

Question 89

What should a tester do if GenAI generates test cases containing assumptions that are not supported by the requirements?

1. Identify the unsupported assumptions and validate or remove them
2. Accept them because assumptions improve creativity
3. Add the assumptions to the requirements automatically
4. Treat every assumption as an approved business rule

Correct Answer: 1

Explanation

Generated test cases can contain assumptions when the supplied requirements or context are incomplete. A tester should identify such assumptions and determine whether they are supported by authoritative information. Unsupported assumptions should not automatically become part of the test basis. They may instead indicate that clarification is needed from business or technical stakeholders. Removing unsupported assumptions helps ensure that testing remains based on actual requirements rather than model-generated interpretations. GenAI can help identify possible scenarios, but the tester must distinguish documented behavior from information that the model has inferred.

Question 90

Which issue should be considered when using GenAI-generated test data containing realistic personal information?

1. Whether the data contains enough spelling variations
2. Whether the generated names are aesthetically pleasing
3. Whether the dataset has exactly the same number of records as production
4. Privacy and data-protection requirements

Correct Answer: 4

Explanation

Realistic personal information can still create privacy and data-protection concerns, even when it is generated or transformed with the assistance of GenAI. Testers should determine whether the data could identify real individuals or expose sensitive attributes and whether its use complies with organizational and regulatory requirements. Synthetic data that does not correspond to real individuals may reduce some risks, but it still needs to satisfy the testing objective. The focus should be on appropriate data handling, minimization, protection, and validation rather than simply making generated data look realistic.

Question 91

Which prompting approach is most suitable when a tester wants the model to follow a specific test-case template demonstrated by several examples?

1. Zero-shot prompting
2. Few-shot prompting
3. Random prompting
4. Unconstrained prompting

Correct Answer: 2

Explanation

Few-shot prompting provides several examples that demonstrate the expected relationship between the input and output. When a tester wants GenAI to follow a particular test-case template, examples can show the desired terminology, structure, level of detail, and formatting. The model can then use those examples as guidance when generating additional cases. This approach can improve consistency, although it does not guarantee correctness. Testers should still validate the generated cases against requirements and ensure that the examples themselves represent the intended testing practice.

Question 92

Why is it useful to specify the intended audience when asking GenAI to produce a testing summary?

1. It guarantees that the summary contains no errors
2. It prevents the model from using technical information
3. It can help the model adjust terminology, detail, and emphasis for the audience
4. It makes human review unnecessary

Correct Answer: 3

Explanation

Different audiences require different levels of detail and terminology. A technical testing team may need information about failed test cases, environments, logs, and defect details, while management may need a concise summary of progress, risks, and major issues. Specifying the intended audience helps GenAI tailor the structure and level of explanation accordingly. However, audience-specific formatting does not guarantee factual accuracy. Testers should verify the summary against the original evidence before distribution. The goal is to make communication more useful while preserving accurate information about the testing activity.

Question 93

Which statement best describes the relationship between GenAI output and testing evidence?

1. Generated output can automatically replace execution evidence
2. Generated output is always stronger evidence than actual test results
3. Testing evidence is unnecessary when a model provides a confident explanation
4. GenAI output should not be treated as a substitute for actual testing evidence

Correct Answer: 4

Explanation

GenAI can generate interpretations, summaries, hypotheses, and testing suggestions, but these outputs do not replace actual testing evidence. Evidence such as execution results, logs, screenshots, requirements, and observed behavior provides the basis for determining what actually happened. A model may produce a convincing explanation that is not supported by those facts. Testers should therefore distinguish between generated suggestions and verified evidence. GenAI can help organize or interpret evidence, but important conclusions should remain grounded in information that can be independently verified.

Question 94

What is a useful way to reduce ambiguity in a prompt for test-case generation?

1. Clearly define the task, scope, relevant requirements, and expected output
2. Remove all constraints from the request
3. Use intentionally vague terminology
4. Provide unrelated examples

Correct Answer: 1

Explanation

A clear prompt should communicate what the tester wants the model to accomplish, which part of the system is in scope, what requirements or business rules should be considered, and how the output should be structured. This reduces the opportunity for the model to make unnecessary assumptions. For example, a tester can specify the user role, feature, input conditions, expected behavior, and desired test-case format. Clear instructions improve the usefulness of generated results but do not guarantee correctness. Human review remains necessary to identify missing or incorrect scenarios.

Question 95

What is one reason to use synthetic test data instead of real production data when working with GenAI?

1. Synthetic data always provides perfect production realism
2. It can reduce exposure to sensitive real-world information
3. Synthetic data never requires validation
4. It automatically meets every business requirement

Correct Answer: 2

Explanation

Synthetic test data can reduce the need to expose real production information, particularly when production datasets contain personal, confidential, or commercially sensitive data. GenAI can assist in creating synthetic values that follow specified formats and characteristics. However, synthetic data still needs to be evaluated for realism, validity, relationships, boundary conditions, and suitability for the intended tests. It may not accurately represent every production characteristic. Therefore, synthetic data is a useful privacy-conscious option in many situations, but it should be designed and validated according to the actual testing objectives.

Question 96

Which activity demonstrates human oversight of a GenAI-generated test suite?

1. Importing every generated test automatically
2. Allowing the model to determine release readiness alone
3. Reviewing generated tests and deciding which are appropriate based on requirements and risks
4. Disabling all testing tools

Correct Answer: 3

Explanation

Human oversight involves reviewing generated results and applying professional judgment before important testing decisions are made. For a GenAI-generated test suite, testers can assess whether individual tests are correct, relevant, non-duplicative, traceable, and aligned with requirements and risks. They can also identify missing scenarios and reject unsuitable cases. Simply importing all generated tests does not constitute meaningful oversight. Similarly, allowing the model to determine release readiness would delegate a significant decision without adequate human evaluation. GenAI should support testing decisions while appropriate responsibility remains with qualified people.

Question 97

Which factor can affect the quality of a GenAI-generated testing response?

1. Only the model’s response length
2. The relevance and quality of the information supplied to the model
3. Only the computer’s screen resolution
4. The number of testers watching the response

Correct Answer: 2

Explanation

The quality of information supplied to a GenAI system can strongly influence the relevance and usefulness of its output. Requirements, business rules, constraints, examples, test objectives, and other accurate context help the model understand the task more effectively. Poor-quality or incomplete information can lead to generic responses, incorrect assumptions, or missing scenarios. However, providing high-quality context does not eliminate model limitations such as hallucination or non-determinism. Testers should therefore combine good prompt design with independent validation and established testing practices.

Question 98

A GenAI model suggests a security test based on a vulnerability that does not apply to the application’s technology stack. What should the tester do?

1. Execute every suggestion regardless of relevance
2. Modify the application to make the vulnerability applicable
3. Add the vulnerability to the security requirements
4. Assess the suggestion against the actual technology and security context before using it

Correct Answer: 4

Explanation

GenAI may generate security suggestions based on general patterns that do not necessarily apply to the specific technology stack or architecture. Testers should assess each suggestion against the actual application technologies, configurations, attack surface, and security requirements. Irrelevant suggestions can be documented or discarded rather than blindly executed. At the same time, testers should avoid assuming that a vulnerability is irrelevant without appropriate technical analysis. GenAI can broaden security-testing ideas, but qualified security assessment is required to determine which scenarios are applicable and meaningful.

Question 99

What is an important advantage of using GenAI to assist with repetitive test documentation tasks?

1. It can reduce manual effort and allow testers to focus on higher-value analysis
2. It eliminates the need to maintain accurate records
3. It guarantees that generated documentation contains no omissions
4. It makes test evidence unnecessary

Correct Answer: 1

Explanation

GenAI can automate or accelerate repetitive documentation activities such as drafting summaries, restructuring test descriptions, and preparing initial reports. This can reduce manual effort and allow testers to spend more time on analysis, investigation, risk assessment, and other activities requiring human judgment. However, generated documentation still needs review for accuracy and completeness. The model may omit important details or introduce unsupported information. Therefore, the productivity benefit comes from assisting with the initial work rather than eliminating the tester’s responsibility for maintaining accurate and trustworthy testing records.

Question 100

Which statement best represents responsible use of GenAI-generated testing artifacts?

1. Treat every generated artifact as an approved testing asset
2. Use generated artifacts only when they are longer than manually created artifacts
3. Review and validate artifacts before relying on them for testing decisions
4. Allow the model to approve its own generated results

Correct Answer: 3

Explanation

Responsible use of GenAI requires appropriate review and validation of generated testing artifacts before they are relied upon for important decisions. Generated test cases, automation code, summaries, defect descriptions, and analysis can contain errors, omissions, unsupported assumptions, or other problems. Testers should compare outputs with authoritative requirements and evidence and apply relevant testing expertise before approving them. The objective is not to reject GenAI, but to use it within a controlled process that combines its ability to accelerate work with human judgment, accountability, and validation.