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Question 121

A tester uses a GenAI tool to generate test cases from a detailed software requirement. What is the most important action before adding the generated test cases to the formal test suite?

1. Increase the number of generated test cases
2. Remove all negative test cases
3. Review the generated cases against the approved requirements
4. Allow the tool to decide which cases are mandatory

Correct Answer: 3

Explanation

GenAI can generate useful test cases quickly, but generated content should not automatically be considered correct or complete. The tester should compare each relevant test case with the approved requirements and determine whether the expected behavior is accurately represented. This review can identify missing scenarios, incorrect assumptions, duplicated cases, or tests that are outside the intended scope. Formal test suites require traceability and consistency with agreed requirements, so human validation remains important. Increasing the number of generated cases does not guarantee better coverage, while removing negative cases can reduce coverage. The AI tool should assist the tester rather than independently determine which tests become part of the official suite.

Question 122

Which characteristic of GenAI output is most directly concerned with whether the generated test cases address the intended testing objectives?

1. Relevance
2. Tokenization
3. Model size
4. Training duration

Correct Answer: 1

Explanation

Relevance describes how closely generated content addresses the requested task, context, and testing objectives. When GenAI produces test cases, relevant cases should focus on the specified functionality, risks, requirements, and intended scope. A test case may be syntactically correct but still be irrelevant if it tests an unrelated feature or assumes behavior that is outside the requirement. Tokenization is a technical process used when handling text, while model size and training duration are characteristics of model development rather than direct measures of whether a particular test case meets the tester’s objective. Evaluating relevance is therefore an important part of reviewing GenAI-generated testing artifacts before using them.

Question 123

A tester asks a GenAI assistant to generate boundary-value test cases but provides no information about the valid input range. What is the primary concern?

1. The model will always generate identical results
2. The model may invent or assume boundary values
3. The model cannot generate numerical test cases
4. The prompt will automatically become a few-shot prompt

Correct Answer: 2

Explanation

Boundary-value analysis depends on knowing the valid and invalid limits of an input. If the tester does not provide those limits, a GenAI model may infer values from general knowledge, patterns in the prompt, or assumptions that are not applicable to the actual system. Such generated boundaries can appear reasonable while being factually incorrect for the application under test. The tester should therefore provide the required domain information or verify the assumptions before using the generated cases. GenAI is capable of producing numerical examples, and the absence of boundaries does not automatically make the prompt few-shot. The main issue is that the model lacks authoritative context for determining correct boundary values.

Question 124

Which prompting approach provides several examples of desired test-case inputs and outputs to guide a GenAI model?

1. Zero-shot prompting
2. Role prompting
3. Constraint prompting
4. Few-shot prompting

Correct Answer: 4

Explanation

Few-shot prompting provides the model with several examples that demonstrate the expected pattern or format of the desired response. In software testing, a tester might provide several existing test cases showing how requirements are converted into test steps, expected results, and priorities. The model can then use those examples as guidance when generating additional cases. Zero-shot prompting provides instructions without examples. Role prompting establishes a perspective or role, such as asking the model to act as a test analyst. Constraint prompting focuses on limitations or rules. Few-shot prompting is especially useful when the tester wants consistent output that follows an established example-based pattern.

Question 125

A GenAI model generates a defect report that contains a specific root cause not supported by the available evidence. What should the tester do?

1. Treat the root cause as confirmed because the model generated it
2. Verify the proposed root cause using available technical evidence
3. Delete all information from the defect report
4. Publish the report without modification

Correct Answer: 2

Explanation

GenAI can produce plausible explanations that are not necessarily supported by evidence. A generated root cause should therefore be treated as a hypothesis until confirmed through appropriate investigation. The tester can examine logs, reproduction steps, source code, configuration, monitoring information, or other relevant evidence to determine whether the proposed cause is justified. Publishing an unsupported root cause could mislead developers and affect defect prioritization or troubleshooting. GenAI can accelerate the drafting and analysis of defect reports, but it should not replace technical verification. Human reviewers remain responsible for ensuring that factual claims in a defect report are accurate, supported, and appropriate for the intended audience.

Question 126

Which practice best reduces the risk of exposing confidential source code when using an external GenAI service?

1. Paste the complete repository into every prompt
2. Remove all security controls before sending the code
3. Follow organizational data-handling rules and minimize shared information
4. Share production credentials together with the code

Correct Answer: 3

Explanation

Confidential source code may contain intellectual property, credentials, business logic, security mechanisms, or other sensitive information. Before using an external GenAI service, testers should follow organizational policies and applicable data-protection requirements. Data minimization is an important practice because the tester should provide only the information necessary for the task. Sensitive values should be removed or appropriately protected when possible. Sending an entire repository unnecessarily increases exposure, while sharing credentials creates a serious security risk. Disabling security controls does not make GenAI usage safer. The tester should understand the approved service, retention rules, access controls, and organizational requirements before submitting proprietary information.

Question 127

A tester repeatedly asks a GenAI model to generate test cases and receives slightly different results from the same prompt. Which characteristic does this demonstrate?

1. Non-deterministic behavior
2. Data anonymization
3. Static analysis
4. Requirement traceability

Correct Answer: 1

Explanation

Generative AI models can produce different outputs for the same or very similar prompts because generation is probabilistic and may involve sampling or other model settings. This behavior is commonly described as non-determinism. It means that a tester should not automatically assume that repeating the same request will produce identical test cases. For testing activities where consistency is important, the tester may need to control relevant settings, provide a stable context, record prompts and outputs, and review generated artifacts. Data anonymization protects sensitive information, static analysis examines code or other artifacts according to defined rules, and traceability links test artifacts to requirements. None of these concepts directly explains changing generated responses.

Question 128

What is the main purpose of providing explicit output constraints in a GenAI prompt used for test-case generation?

1. To prevent the model from processing the prompt
2. To define the expected structure and limitations of the response
3. To guarantee that every generated test case is correct
4. To eliminate the need for tester review

Correct Answer: 2

Explanation

Output constraints help guide the GenAI model toward a predictable structure and defined boundaries. For example, a tester can request that each test case contain an identifier, preconditions, steps, expected results, and priority, while restricting the number of cases or requiring a particular format. Such constraints can improve consistency and make generated content easier to review or integrate into testing workflows. However, constraints do not guarantee factual correctness, completeness, or suitability. Generated results still require appropriate human evaluation. Similarly, constraints do not remove the need for validation. Their purpose is to communicate clearly what form and limitations the expected output should follow.

Question 129

A tester asks GenAI to generate tests from requirements containing ambiguous terms such as “fast” and “user-friendly.” What is the most appropriate response?

1. Ask GenAI to invent precise definitions
2. Ignore the ambiguous requirements
3. Clarify the requirements or obtain measurable acceptance criteria
4. Generate tests based only on common industry assumptions

Correct Answer: 3

Explanation

Ambiguous requirements can lead to inconsistent interpretations and unsuitable test cases. Terms such as “fast,” “easy,” or “user-friendly” do not necessarily define measurable acceptance criteria. If GenAI is asked to generate tests from such requirements, it may create assumptions that appear reasonable but are not approved by stakeholders. The tester should first clarify the intended meaning and, where possible, obtain measurable criteria such as response-time limits or usability requirements. GenAI can help identify ambiguous wording and suggest questions for clarification, but it should not independently establish business requirements. Clearer requirements improve both human-designed and AI-assisted test design and provide a stronger basis for traceability.

Question 130

Which activity is an appropriate use of GenAI during test documentation?

1. Automatically approving undocumented requirements
2. Replacing all test evidence with generated summaries
3. Generating a draft summary from verified test results
4. Creating results without executing any tests

Correct Answer: 3

Explanation

GenAI can assist with documentation by transforming verified information into readable summaries, status reports, release notes, or other testing artifacts. For example, a tester can provide confirmed execution results and ask the model to produce a concise test summary for stakeholders. The generated summary should then be reviewed to ensure that it accurately reflects the underlying evidence. GenAI should not fabricate test execution results or replace actual evidence with generated statements. Similarly, it should not approve requirements simply because it can produce convincing text. A useful approach is to treat GenAI as a documentation assistant that works from authoritative information while retaining human responsibility for accuracy and completeness.

Question 131

Which risk occurs when a GenAI model produces convincing information that is factually incorrect?

1. Hallucination
2. Tokenization
3. Embedding
4. Compilation

Correct Answer: 1

Explanation

A hallucination occurs when a GenAI model generates information that appears plausible but is unsupported, inaccurate, or fabricated. In software testing, hallucinations can affect requirements interpretation, test cases, defect analysis, automation scripts, or technical explanations. Because generated content can sound confident and professional, testers should not treat fluency as evidence of correctness. Important outputs should be compared with authoritative requirements, system behavior, documentation, execution evidence, or other trusted sources. Tokenization is the process of representing text as tokens, embeddings represent information in numerical form, and compilation transforms source code into another executable or intermediate representation. These concepts do not describe fabricated factual content.

Question 132

A tester wants GenAI to generate test cases specifically for authentication, authorization, and session-management risks. Which prompt element would be most useful?

1. A request to make the answer longer
2. A clear testing scope and relevant security context
3. A request to avoid all negative scenarios
4. An instruction to use unrelated examples

Correct Answer: 2

Explanation

GenAI output becomes more useful when the prompt clearly identifies the intended scope and context. For security-oriented testing, specifying authentication, authorization, session management, relevant user roles, expected security controls, and applicable requirements can help the model focus on meaningful scenarios. A longer response does not necessarily improve relevance or quality. Avoiding negative scenarios would reduce security coverage because many security tests depend on invalid or unauthorized behavior. Unrelated examples may introduce irrelevant assumptions. The tester should also review generated security tests carefully because the model may omit important threats or suggest technically inappropriate approaches. Clear scope and context provide better guidance without replacing expert security review.

Question 133

What is a key benefit of using synthetic test data generated by GenAI instead of copying real customer records?

1. It always represents every possible production scenario
2. It removes the need for data validation
3. It can reduce exposure of real personal information
4. It guarantees that generated data is statistically perfect

Correct Answer: 3

Explanation

Synthetic test data can be generated without directly copying real customer records, which can reduce the risk of exposing personal or confidential information during testing. This can be particularly useful when realistic-looking names, addresses, account values, transactions, or other data structures are required. However, synthetic data still needs validation to ensure that it is suitable for the testing objective. It does not automatically represent every production scenario, and it cannot guarantee perfect statistical characteristics. Testers should also ensure that generated data does not unintentionally reproduce sensitive information. The key benefit is that synthetic data can provide useful test inputs while reducing dependence on actual personal or confidential records.

Question 134

A team wants to evaluate whether GenAI-generated test cases are improving testing efficiency. Which measurement would be most useful?

1. The number of words in each prompt
2. The model’s brand name
3. The number of tokens used by the model
4. Time saved while maintaining acceptable test quality

Correct Answer: 4

Explanation

Evaluating GenAI in testing should consider both efficiency and the quality of the resulting work. Measuring time saved while ensuring that test coverage, correctness, relevance, and other required quality criteria remain acceptable provides useful evidence of practical benefit. A tool that produces many test cases quickly may still create additional review effort or introduce errors. Prompt length, model branding, and token counts may be useful technical metrics in specific contexts, but they do not directly demonstrate whether the testing process has become more effective. A balanced evaluation should consider productivity together with quality, risk, review effort, and the impact on the overall testing workflow.

Question 135

Which prompt is most likely to produce traceable test cases from a software requirement?

1. “Create some tests.”
2. “Write as many tests as possible.”
3. “Create tests and include the requirement ID for each case.”
4. “Generate interesting testing ideas.”

Correct Answer: 3

Explanation

Traceability requires a clear relationship between a requirement and the testing artifacts created to verify it. A prompt that explicitly requests the requirement ID for each generated test case gives the model a structural instruction supporting this relationship. The tester should still verify that the identifiers are real and correctly associated with the approved requirements. Generic prompts may produce useful ideas but do not necessarily create traceable artifacts. Asking for a large number of tests also does not ensure traceability. Including identifiers, expected behavior, and other relevant metadata in the prompt can make generated test cases easier to review, organize, and map back to their originating requirements.

Question 136

A tester uses GenAI to generate automation code for a new API test. What should the tester do before executing the generated code in a shared test environment?

1. Review and validate the generated code and its dependencies
2. Execute it immediately because generated code is tested by the model
3. Remove all assertions from the generated code
4. Give the generated code production credentials

Correct Answer: 1

Explanation

Generated automation code may contain incorrect assumptions, unsuitable libraries, insecure practices, invalid API calls, or errors in assertions and data handling. Before execution in a shared environment, the tester should review the code, understand its behavior, validate dependencies, check environment configuration, and ensure that it follows applicable coding and security standards. Generated code should not be considered automatically tested simply because it came from a GenAI model. Removing assertions reduces the ability to detect failures, while using production credentials unnecessarily increases risk. Human review and controlled execution are particularly important when generated code interacts with external systems, databases, services, or environments containing valuable information.

Question 137

Which situation is an example of over-reliance on GenAI in software testing?

1. Using GenAI to brainstorm additional test ideas
2. Reviewing generated test cases before execution
3. Treating generated test results as correct without independent verification
4. Comparing generated cases with approved requirements

Correct Answer: 3

Explanation

Over-reliance occurs when testers place excessive trust in GenAI output and reduce necessary human validation. Treating generated test results as correct without independent verification is a clear example because the model can produce inaccurate assumptions, incomplete scenarios, or misleading conclusions. GenAI is useful for brainstorming, drafting, transforming information, and accelerating repetitive tasks, but its output should be evaluated according to the risk and importance of the activity. Reviewing generated test cases and comparing them with approved requirements are examples of appropriate oversight. Maintaining human accountability helps prevent errors from being accepted simply because the generated content is fluent or appears technically convincing.

Question 138

A tester asks GenAI to summarize a large collection of defect reports. Which additional instruction can help make the summary more useful?

1. Specify the intended audience and required summary structure
2. Ask the model to remove all repeated information without review
3. Require the model to invent missing defect details
4. Tell the model to ignore severity and priority

Correct Answer: 1

Explanation

Specifying the intended audience and desired structure helps GenAI produce a summary that is relevant and usable. A technical team may need information about recurring failure patterns, affected components, severity, and technical observations, while management may require high-level trends and risk information. The tester can define the required fields, grouping method, time period, and level of detail. The generated summary should still be checked against the original defect reports because summarization can omit important information or introduce inaccuracies. Inventing missing details is inappropriate, and ignoring severity or priority may remove information that is important for interpreting defect trends and testing risks.

Question 139

Which action best supports reproducibility when using GenAI to assist with test design?

1. Keep only the final generated test cases
2. Record the relevant prompt, context, model information, and output
3. Change the prompt after every generation without documentation
4. Avoid recording which model was used

Correct Answer: 2

Explanation

Reproducibility is improved when the information needed to understand or repeat an AI-assisted activity is recorded. Depending on the tool and organizational requirements, this may include the prompt, relevant input context, model or service version, important settings, date, and generated output. Keeping such information allows testers to investigate differences between results and understand how an artifact was produced. Exact reproduction may still be difficult because GenAI can be non-deterministic or because model versions can change. Nevertheless, recording the relevant details provides valuable traceability. Keeping only the final test cases or changing prompts without documentation makes it harder to understand and reproduce the generation process.

Question 140

A GenAI assistant suggests a test approach that conflicts with an approved organizational testing policy. What should the tester do?

1. Follow the AI recommendation because it may be more modern
2. Ignore the organizational policy if the test seems efficient
3. Ask the model to decide which rule should apply
4. Follow the approved policy and investigate the conflict with the appropriate stakeholders

Correct Answer: 4

Explanation

Organizational policies and approved processes establish constraints that AI-generated suggestions must respect. If a GenAI recommendation conflicts with an approved testing policy, the tester should not override the policy simply because the suggestion appears efficient or modern. The conflict should be reviewed with the appropriate stakeholders if clarification or policy change is needed. GenAI can suggest alternatives, but it does not have authority to replace organizational governance. Following approved policies helps maintain consistency, compliance, security, and accountability. The tester should document relevant decisions where required and ensure that any change to an established process is formally reviewed rather than being introduced solely because an AI-generated recommendation appears reasonable.