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Question 61

Which factor is most important when deciding whether a GenAI-generated test case should be added to the formal test suite?

1. Whether the test case is the longest generated case
2. Whether it is relevant, correct, and aligned with the test objectives
3. Whether the model generated it without any prompt
4. Whether it contains technical terminology

Correct Answer: 2

Explanation

A generated test case should be included in a formal test suite only after it has been evaluated against the relevant testing objectives and requirements. The tester should consider whether the scenario is valid, useful, sufficiently specific, and capable of providing meaningful coverage. A long or technically worded test case is not necessarily valuable. GenAI can produce plausible but irrelevant or incorrect cases, so human review remains necessary. The tester should also consider duplication, maintainability, traceability, and risk coverage before deciding whether the generated case belongs in the approved test suite.

Question 62

What is one benefit of using GenAI to generate negative test scenarios?

1. It guarantees that all invalid conditions will be identified
2. It removes the need for negative testing expertise
3. It can suggest additional invalid inputs and unexpected user behaviors
4. It automatically confirms that every negative test will fail

Correct Answer: 4

Explanation

GenAI can assist testers by suggesting invalid inputs, unusual user actions, unexpected sequences, and other negative scenarios. These suggestions may broaden the tester’s initial set of ideas and help identify situations that deserve investigation. However, GenAI cannot guarantee complete identification of negative conditions. Testers should evaluate each suggestion against the application’s requirements, business rules, and risk profile. They should also verify the expected behavior of the system rather than assuming that a generated negative test will necessarily produce a failure. Human analysis remains essential when determining meaningful negative testing coverage.

Question 63

A tester provides a requirement, its business context, and three examples of correctly written test cases before asking GenAI to generate additional cases. Which prompting approach is being used?

1. Few-shot prompting
2. Zero-shot prompting
3. Random sampling
4. Model fine-tuning

Correct Answer: 1

Explanation

Providing several examples of the desired input and output behavior is known as few-shot prompting. In this case, the examples demonstrate how requirements should be translated into test cases and give the model additional context about the expected structure and level of detail. Few-shot prompting can be useful when a tester wants generated results to follow a particular pattern. It does not change the underlying model or guarantee that the generated cases are correct. The tester should still validate the resulting cases against the requirements and testing objectives.

Question 64

Which situation represents an appropriate use of GenAI for test maintenance?

1. Allowing the model to approve every maintenance change automatically
2. Asking GenAI to identify potentially outdated test steps after a requirement change
3. Deleting all existing tests after a system update
4. Assuming old tests remain correct without review

Correct Answer: 2

Explanation

When requirements or application behavior change, existing tests may become outdated. GenAI can help identify test steps, expected results, or scenarios that may need review after such changes. For example, it can compare updated requirements with existing test descriptions and suggest potentially affected cases. The suggestions should then be validated by testers because the model may miss dependencies or incorrectly identify affected areas. GenAI can therefore reduce the manual effort involved in maintenance analysis while keeping human review in the process.

Question 65

What is a possible limitation of using GenAI to generate requirements-based test cases?

1. It may misunderstand ambiguous or incomplete requirements
2. It can never process natural-language requirements
3. It automatically detects every ambiguity
4. It always generates complete traceability information

Correct Answer: 1

Explanation

Ambiguous or incomplete requirements create uncertainty for both human testers and GenAI systems. A model may interpret unclear wording in a way that differs from the intended business behavior and then generate test cases based on that interpretation. The resulting cases may therefore appear reasonable while testing the wrong behavior. Testers should identify and clarify ambiguous requirements before relying on generated tests. GenAI can also be used to highlight possible ambiguities, but its suggestions need human confirmation. Clear, approved requirements provide a stronger foundation for useful test generation.

Question 66

Which activity can GenAI support when analyzing a large collection of defect reports?

1. Automatically determining legal responsibility for every defect
2. Guaranteeing the true root cause of each defect
3. Replacing all defect management processes
4. Summarizing recurring themes and potentially similar defect descriptions

Correct Answer: 4

Explanation

GenAI can assist with analyzing large collections of defect reports by summarizing recurring themes, grouping potentially similar descriptions, or highlighting common terminology. This can help testers and development teams identify areas that may deserve further investigation. However, similarity in wording does not necessarily mean that defects have the same technical root cause. Generated summaries and groupings should therefore be reviewed against the original defect evidence. GenAI is useful for reducing the effort required to examine large amounts of textual information, but it should not be treated as an authoritative source for defect causation.

Question 67

Why should testers be careful when asking GenAI to generate test data based on real customer information?

1. Real customer information may contain sensitive or personally identifiable data
2. GenAI cannot generate numerical values
3. Customer information can never be transformed into test data
4. Test data does not require privacy protection

Correct Answer: 1

Explanation

Customer information may contain personally identifiable information, confidential business information, financial details, credentials, or other sensitive data. Sending such information to a GenAI service can create privacy and security risks, particularly when the service is external or not approved for sensitive processing. Testers should follow organizational policies and applicable data-protection requirements. Where appropriate, anonymization, masking, synthetic data, or approved secure environments can reduce exposure. The objective is to obtain useful test data without unnecessarily disclosing sensitive information. Privacy requirements should therefore be considered before real customer information is provided to GenAI.

Question 68

Which approach can improve the consistency of GenAI-generated test cases across multiple requests?

1. Removing all requirements from each prompt
2. Using clear instructions, consistent context, and a defined output structure
3. Changing the terminology randomly for every request
4. Asking the model to ignore previous constraints

Correct Answer: 2

Explanation

Consistency can be improved by using standardized prompts that contain clear instructions, relevant context, constraints, terminology, and output structures. For example, a testing team can define a prompt template requiring every generated case to include an identifier, objective, preconditions, steps, data, and expected result. Such standardization helps reduce unnecessary variation and makes outputs easier to compare and review. However, GenAI may still produce variations because generation can be probabilistic. Therefore, standardized prompting should be combined with human validation and, where needed, controlled model settings and documented procedures.

Question 69

What is the main purpose of defining constraints in a GenAI prompt for test generation?

1. To prevent the model from processing the task
2. To specify boundaries or conditions that the generated output should follow
3. To guarantee that every generated test is correct
4. To eliminate the need for requirements

Correct Answer: 2

Explanation

Constraints define boundaries or conditions that the GenAI output should follow. For example, a tester may specify that test cases must focus only on a particular user role, include both valid and invalid inputs, or use a specified format. Constraints help narrow the generation task and make the resulting output more relevant to the intended testing objective. They do not guarantee correctness or completeness. Testers should still evaluate the generated results against the authoritative requirements. Clear constraints are particularly helpful when the task could otherwise produce a very broad or unfocused set of testing suggestions.

Question 70

A tester asks GenAI to generate tests for a payment feature but does not mention currency limits, user permissions, or transaction rules. What is a likely result?

1. The model will automatically obtain all missing business rules
2. The model may generate generic tests that overlook important conditions
3. The model will always refuse to generate tests
4. The generated tests will automatically contain complete coverage

Correct Answer: 4

Explanation

When important business rules are missing from the prompt or available context, GenAI may generate general test scenarios that fail to address important conditions. Payment functionality can involve limits, currencies, permissions, transaction states, validation rules, and error handling. If these details are not supplied, the model may make assumptions or simply omit them. Testers should provide relevant requirements and constraints whenever possible. After generation, the test cases should be reviewed against authoritative business rules to identify missing scenarios and incorrect assumptions. Context quality has a significant effect on the usefulness of generated testing artifacts.

Question 71

Which statement best describes model bias as a risk in GenAI-supported testing?

1. The model may systematically produce outputs influenced by patterns or biases present in its data or design
2. Bias means that the model cannot generate text
3. Bias guarantees equal treatment of every scenario
4. Bias only affects software compilation

Correct Answer: 3

Explanation

GenAI models can reflect patterns, assumptions, or biases present in their training data, development processes, or other sources influencing their behavior. In testing, this could result in certain user groups, scenarios, languages, or behaviors receiving less useful coverage. Testers should consider whether generated test ideas adequately represent the relevant population and system behavior. Diverse requirements, examples, datasets, and human review can help identify potential gaps. Bias does not necessarily mean that every output is incorrect, but it is an important consideration when assessing whether GenAI-generated testing artifacts provide sufficiently broad and appropriate coverage.

Question 72

Which practice is most useful for evaluating the factual accuracy of a GenAI-generated explanation of a test failure?

1. Accepting the explanation because it is detailed
2. Comparing the explanation with actual test evidence and system information
3. Asking the same model to confirm its own answer
4. Increasing the number of adjectives in the prompt

Correct Answer: 2

Explanation

A GenAI-generated explanation of a test failure should be treated as a hypothesis until it is supported by evidence. Testers can compare the explanation with execution logs, screenshots, error messages, source code, requirements, configuration information, and reproduction results. This independent evidence can confirm or contradict the model’s interpretation. A detailed explanation may still be hallucinated or based on incorrect assumptions. Asking the same model to confirm its response does not provide independent verification. Evidence-based review is therefore essential when GenAI is used to interpret failures or suggest possible causes.

Question 73

What is one reason to preserve the prompt used to generate an important testing artifact?

1. It can support traceability and help reproduce or understand the generation process
2. It guarantees that the model will always produce the same output
3. It removes the need for human review
4. It automatically proves the generated artifact is correct

Correct Answer: 1

Explanation

Preserving the prompt used to generate an important testing artifact can support traceability and help others understand how the artifact was created. It may also assist in reproducing the interaction, investigating unexpected output, or refining the prompt later. Depending on the GenAI tool and settings, preserving additional information such as model version, relevant context, and configuration may also be useful. Keeping a prompt does not guarantee identical future output because generation can vary. It also does not establish that the resulting artifact is correct, so normal testing review and validation remain necessary.

Question 74

Which task is generally suitable for GenAI assistance when preparing test documentation?

1. Automatically approving the final documentation without review
2. Generating a preliminary summary of test activities from provided information
3. Inventing test results that are not available
4. Replacing all evidence with generated text

Correct Answer: 2

Explanation

GenAI can assist with documentation by producing preliminary summaries from information supplied by the tester. For example, it may summarize test objectives, execution activities, notable results, or identified issues. This can reduce the effort required to create an initial draft. However, generated documentation should be checked against the original evidence because the model may omit important information or introduce unsupported statements. Test results must never be invented simply to complete a document. The final documentation should accurately represent what actually occurred during testing and should follow applicable organizational reporting requirements.

Question 75

What is an important consideration when using an external GenAI service for proprietary source code analysis?

1. Whether sending the code is permitted under security, confidentiality, and intellectual-property requirements
2. Whether the model can produce a response in exactly one second
3. Whether the source code contains enough comments for the model to memorize it
4. Whether the service automatically becomes the code owner

Correct Answer: 1

Explanation

Proprietary source code may contain confidential business logic, intellectual property, security-sensitive information, or third-party material. Before sending it to an external GenAI service, an organization should determine whether such processing is permitted by security policies, contracts, confidentiality agreements, and intellectual-property requirements. The organization may need an approved enterprise service, specific data-handling controls, or restrictions on what code can be submitted. The technical ability of the model to analyze the source code is only one consideration. Governance and data protection requirements must also be addressed.

Question 76

Which statement about GenAI-generated test coverage is correct?

1. A large number of generated tests automatically means high coverage
2. Generated tests can contain duplicates or miss important scenarios
3. GenAI always identifies every branch and boundary condition
4. Test coverage no longer requires measurement when GenAI is used

Correct Answer: 2

Explanation

GenAI can generate many test cases quickly, but quantity does not guarantee meaningful coverage. Generated tests may duplicate existing scenarios, focus heavily on common paths, or overlook important branches, boundaries, error conditions, and business rules. Testers should evaluate generated cases using appropriate coverage criteria and testing techniques. Where applicable, requirements traceability, code coverage, risk analysis, and other measures can help identify gaps. GenAI should therefore be viewed as a tool for generating ideas and artifacts rather than as a replacement for systematic test analysis and coverage evaluation.

Question 77

A tester uses GenAI to convert manual test steps into automation code. What should happen before the code becomes part of the approved automation suite?

1. It should be reviewed and executed to confirm correct behavior
2. It should immediately replace all existing automation
3. It should be accepted because generated code is guaranteed to compile
4. It should be deployed directly to production

Correct Answer: 1

Explanation

Generated automation code should be reviewed and executed before being incorporated into an approved automation suite. The tester or developer should verify syntax, framework compatibility, locators, test logic, assertions, data handling, error handling, and maintainability. Execution helps reveal problems that may not be obvious from reading the code. The generated code should also be compared with the intended manual test to ensure that important behavior has not been lost during conversion. GenAI can significantly accelerate automation development, but human review and validation remain necessary for reliable automation.

Question 78

What can happen if a GenAI model is given irrelevant context along with the relevant testing information?

1. Irrelevant information can distract the generation process and reduce output relevance
2. The model automatically removes every irrelevant detail
3. Irrelevant context guarantees more complete test coverage
4. The model stops using natural language

Correct Answer: 4

Explanation

Irrelevant context can make it harder for a GenAI model to focus on the information that actually matters for the requested task. This can contribute to less relevant, inconsistent, or confusing outputs. Testers should therefore aim to provide context that is both sufficient and relevant rather than simply supplying as much information as possible. Clear prompts can identify the task, important requirements, constraints, and expected format while avoiding unnecessary material. Even with carefully selected context, generated outputs should still be reviewed because relevance of input does not guarantee correctness of output.

Question 79

Which approach can help a testing team establish responsible use of GenAI?

1. Allowing every tester to submit any confidential information
2. Defining organizational guidelines covering approved uses, data handling, security, and review
3. Prohibiting all human review of generated outputs
4. Allowing generated results to become requirements automatically

Correct Answer:2

Explanation

Responsible GenAI use benefits from clear organizational guidelines. These guidelines can define approved tools, acceptable use cases, prohibited data, privacy and security requirements, intellectual-property considerations, review responsibilities, and escalation procedures. Such governance helps testers understand how GenAI can be incorporated safely into existing processes. Guidelines should also clarify that generated outputs require appropriate validation before being treated as testing artifacts or evidence. Responsible use is not simply about restricting GenAI; it is about establishing controls that balance its benefits with risks such as data leakage, hallucination, bias, and inappropriate reliance.

Question 80

A GenAI tool produces a test case that conflicts with an approved requirement. What should the tester do?

1. Change the approved requirement to match the generated test
2. Ignore the conflict and execute the test anyway
3. Validate the conflict against the authoritative requirement and correct or reject the generated case
4. Assume that GenAI has identified a newer requirement automatically

Correct Answer: 3

Explanation

An approved requirement is an authoritative source for determining the intended behavior unless an authorized change has been made. If a GenAI-generated test case conflicts with that requirement, the tester should investigate the discrepancy and validate the generated case against the approved information. The generated test should be corrected, rejected, or escalated as appropriate. Testers should not change requirements simply because a model produced a different interpretation. GenAI can identify possible questions or inconsistencies, but it does not automatically establish a new requirement. Human governance and approved change processes remain essential.