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Question 341

A tester asks GenAI to identify missing test scenarios from an existing test suite. What should be provided to improve the analysis?

1. Only the test case titles
2. The current test suite, relevant requirements, and testing scope
3. The tester’s personal preferences
4. An unrelated previous project

Correct Answer: 2

Explanation

Gap analysis is more effective when GenAI has sufficient context about what is being tested. Providing the existing test suite together with relevant requirements and the defined testing scope allows the model to compare intended behavior with existing coverage. Titles alone may omit important conditions, expected results, or business rules. An unrelated project can introduce irrelevant assumptions. Even with good context, the generated gap analysis should be reviewed because GenAI may overlook subtle conditions or incorrectly identify a gap. Testers should confirm proposed missing scenarios against authoritative requirements and risk information before adding them to the formal test suite.

Question 342

Which prompt characteristic is most useful when asking GenAI to generate a defect report?

1. A clearly defined structure containing required defect fields
2. A request to use as many technical terms as possible
3. A request to make the report extremely long
4. No information about the observed behavior

Correct Answer: 1

Explanation

A clearly defined output structure helps GenAI organize defect information consistently. The prompt can specify fields such as summary, environment, preconditions, steps to reproduce, actual result, expected result, severity, and supporting evidence. This can improve readability and make generated reports easier to review. However, structure does not guarantee factual correctness. The tester must verify that every detail reflects the actual observed behavior and available evidence. GenAI should not invent reproduction steps, environment details, or severity. A structured prompt therefore supports consistency, while human verification ensures that the resulting defect report remains accurate and useful.

Question 343

A GenAI-generated test case includes an expected result that is not specified anywhere in the available requirements. What should the tester do?

1. Treat the generated expected result as the official requirement
2. Delete all expected results from the test suite
3. Verify the expected behavior with an authoritative source
4. Assume the model inferred it correctly

Correct Answer: 3

Explanation

An expected result that is not supported by the available requirements should be treated as an assumption until verified. The tester should consult an authoritative specification, product owner, domain expert, or other appropriate source to establish the intended behavior. GenAI can infer plausible outcomes, but plausible does not mean correct. Accepting unsupported expectations can cause valid application behavior to be incorrectly reported as a defect or can result in testing against an unintended rule. Verification also improves traceability because the expected result can then be connected to an approved requirement or business rule.

Question 344

A tester needs GenAI to generate tests for a mobile application that behaves differently on Android and iOS. What is important to include in the prompt?

1. Only the application name
2. The tester’s preferred operating system
3. Platform-specific behavior and supported versions
4. The number of test cases already written

Correct Answer: 3

Explanation

Platform-specific information is important when application behavior differs between Android and iOS. The prompt should identify the supported platforms, relevant versions, and any known differences in functionality, permissions, navigation, notifications, or device behavior. Without this context, GenAI may produce generic scenarios that fail to address platform-specific risks. The generated tests should then be checked against the application’s compatibility requirements and actual supported configurations. The number of existing test cases does not replace platform context. Providing accurate environment information helps GenAI generate more relevant scenarios while still requiring testers to validate the resulting coverage.

Question 345

Which activity is most appropriate when evaluating whether GenAI-generated test cases are useful for a project?

1. Measuring only how quickly they were generated
2. Comparing them against defined quality and coverage criteria
3. Counting the number of words in each test
4. Accepting all cases with technical terminology

Correct Answer: 2

Explanation

The usefulness of generated test cases should be evaluated against defined quality criteria. Relevant factors can include correctness, requirement coverage, expected-result accuracy, uniqueness, maintainability, risk coverage, and suitability for execution. Generation speed can be useful as a productivity metric, but it does not demonstrate that the resulting tests are valuable. Similarly, word count and technical terminology are poor indicators of test quality. A defined evaluation approach allows teams to compare GenAI-assisted outputs with their existing testing standards. Human review remains necessary because automated or model-based evaluation may not fully understand project-specific risks and business rules.

Question 346

A tester wants GenAI to generate tests for an application’s localization features. Which context is most useful?

1. Supported languages, locales, formats, and localization requirements
2. The tester’s favorite language
3. Only the application’s logo
4. The number of developers on the project

Correct Answer: 1

Explanation

Localization testing depends on language and regional requirements. Useful context includes supported languages, locales, date and time formats, number formats, currencies, character sets, text direction, and known localization rules. Providing this information enables GenAI to suggest scenarios that are relevant to the application’s supported environments. The tester should still verify generated cases against the official localization requirements because the model may introduce unsupported locales or assumptions. Localization defects can involve both functional and presentation issues, so the generated tests should cover meaningful behavior rather than focusing only on translated text.

Question 347

A team is concerned that different GenAI responses make it difficult to reproduce a previous test-generation result. Which practice can improve reproducibility?

1. Record the prompt, relevant context, model/version information, and generated output
2. Change the prompt every time
3. Delete previous generated results
4. Avoid documenting GenAI usage

Correct Answer: 1

Explanation

Generative systems can produce different results from similar requests. Recording the prompt, supplied context, model or service version, relevant configuration, and resulting output creates a useful record for later comparison. Where the tool supports additional controls, those settings may also be recorded. This does not guarantee identical output, but it improves the ability to understand and investigate differences. Reproducibility is particularly important for regulated or high-risk testing activities where teams may need to explain how an artifact was produced. Proper records also support review, auditing, troubleshooting, and continuous improvement of GenAI-assisted testing practices.

Question 348

A tester provides GenAI with a large amount of unrelated project information before requesting test cases. What problem can this cause?

1. Excessive irrelevant context can reduce the relevance of the generated output
2. The model will always become more accurate
3. The test cases will automatically have complete coverage
4. Additional context eliminates hallucinations

Correct Answer: 1

Explanation

More context is not always better. Large amounts of irrelevant information can make it harder for GenAI to focus on the information that directly affects the requested testing task. This can result in less relevant scenarios, inconsistent interpretations, or unnecessary content. Testers should provide sufficient but targeted context, emphasizing applicable requirements, constraints, terminology, and expected behavior. Removing irrelevant information can make prompts clearer and easier to review. Context management does not eliminate hallucinations or guarantee complete coverage, so generated outputs still require validation against authoritative project information and established testing objectives.

Question 349

A tester asks GenAI to create tests for an authentication feature. Which scenario is particularly relevant for security-focused testing?

1. Changing the application’s theme
2. Attempting authentication with invalid or manipulated credentials
3. Sorting an unrelated product list
4. Changing the user’s preferred font

Correct Answer: 2

Explanation

Authentication testing should include scenarios that challenge the application’s handling of invalid, manipulated, expired, or otherwise unauthorized credentials. These scenarios can help identify weaknesses in authentication controls and error handling. GenAI may suggest additional cases such as account lockout, session behavior, password policies, and authentication bypass attempts. However, generated security scenarios must be reviewed by qualified testers and aligned with the application’s security requirements and authorized testing boundaries. Testing unrelated presentation or functional features does not directly address authentication security. The goal is to generate meaningful security scenarios while ensuring that testing remains controlled and appropriate.

Question 350

What is a key benefit of using GenAI to transform requirements into potential test conditions?

1. It guarantees that every requirement will be correctly interpreted
2. It can help testers identify possible scenarios and conditions more quickly
3. It removes the need for requirement reviews
4. It automatically approves the resulting test conditions

Correct Answer: 2

Explanation

GenAI can accelerate the transformation of textual requirements into potential test conditions. It can identify obvious positive and negative scenarios, boundary conditions, combinations, and questions that may help testers explore the requirement. However, it does not guarantee correct interpretation or complete coverage. Ambiguous requirements may produce incorrect assumptions, while important domain-specific conditions may be missed. Therefore, testers should review generated conditions against approved requirements and risk information before using them. GenAI is best viewed as an assistance mechanism that can increase analysis efficiency while leaving interpretation, validation, and final test decisions under appropriate human oversight.

Question 351

A tester receives a GenAI-generated explanation for why a test failed. What should be done before recording it as the defect root cause?

1. Verify the explanation using test evidence and technical investigation
2. Accept it because GenAI provided a detailed explanation
3. Record every generated hypothesis as a confirmed cause
4. Ignore the actual test logs

Correct Answer: 1

Explanation

A generated explanation is a hypothesis rather than proof of root cause. The tester should examine logs, application behavior, configuration, reproduction results, code, and other available evidence to determine whether the proposed explanation is supported. GenAI can help generate possible causes and investigation ideas, but it may confidently present an incorrect explanation. Recording an unverified hypothesis as the confirmed root cause can mislead developers and affect corrective actions. Human investigation remains essential for defect analysis. The generated explanation can be retained as an investigation aid when clearly identified as a hypothesis until sufficient evidence confirms or disproves it.

Question 352

A tester wants GenAI to generate tests for an API with several mandatory and optional parameters. Which information should be emphasized?

1. Only the API’s display name
2. Parameter rules, valid values, dependencies, and expected responses
3. The team’s vacation schedule
4. The application’s color scheme

Correct Answer: 2

Explanation

API test generation benefits from detailed information about parameter behavior. Mandatory and optional parameters, allowed values, formats, dependencies, invalid combinations, authentication requirements, and expected responses provide useful context. This enables GenAI to suggest positive, negative, boundary, and combination scenarios. Without these details, the model may invent unsupported values or overlook important interactions. Generated cases should still be checked against the authoritative API specification and actual implementation. Testers should also consider response codes, error messages, data validation, and security requirements where applicable. Detailed API context therefore improves the relevance of generated tests without removing the need for review.

Question 353

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

1. Using GenAI to brainstorm scenarios and then reviewing them
2. Using GenAI to draft test documentation and verifying the content
3. Accepting generated test cases without checking them against requirements
4. Asking GenAI to suggest additional boundary conditions

Correct Answer: 3

Explanation

Over-reliance occurs when testers treat generated content as authoritative without performing appropriate validation. Accepting test cases without checking them against requirements can allow hallucinations, omissions, incorrect assumptions, or outdated information into the testing process. Using GenAI for brainstorming or drafting can be appropriate when the output is reviewed and corrected. Testers should understand both the benefits and limitations of the technology and retain responsibility for the quality of the resulting artifacts. The level of human oversight should be appropriate to the risk of the testing activity, particularly when generated content affects important decisions or high-risk systems.

Question 354

A tester asks GenAI to summarize a large set of test execution results. Which information should be verified before the summary is distributed?

1. Pass, fail, blocked, and other reported execution figures
2. The model’s preferred writing style
3. The number of words in the summary
4. The model’s training history

Correct Answer: 1

Explanation

Execution statistics are factual information that should come from authoritative test records. Before distributing a generated summary, the tester should verify counts such as passed, failed, blocked, skipped, or not-run tests against the actual execution data. Important defect references and scope information should also be checked. GenAI may accidentally omit, duplicate, or misinterpret numerical information while summarizing large datasets. Verification prevents inaccurate metrics from reaching stakeholders. The generated summary can still provide significant productivity benefits by improving readability and organization, but the underlying figures and factual statements must remain consistent with the source records.

Question 355

A tester is creating prompts for recurring regression-test generation. Which practice can improve maintainability?

1. Store reusable prompt templates with clearly defined variables and instructions
2. Rewrite every prompt from scratch without documentation
3. Remove all project terminology
4. Keep successful prompts private from the rest of the team

Correct Answer: 1

Explanation

Reusable prompt templates can make recurring GenAI-assisted activities more consistent and maintainable. Variables can represent items such as feature names, requirements, environments, supported versions, or risk categories. Clear instructions and expected output structures also help testers obtain comparable results over time. Templates should be version-controlled or otherwise documented so that changes can be reviewed. They should not contain unnecessary sensitive information. Maintaining reusable prompts does not guarantee identical responses, but it creates a more controlled process and makes successful prompting practices easier to share, review, and improve across the testing team.

Question 356

A tester asks GenAI to generate tests from a requirement containing contradictory statements. What should happen first?

1. Ask GenAI to choose whichever statement appears more reasonable
2. Clarify the contradiction with the appropriate requirement authority
3. Generate tests for both interpretations and treat both as official
4. Ignore the contradiction

Correct Answer: 2

Explanation

Contradictory requirements should be resolved by the appropriate requirement owner, product stakeholder, or other authoritative source before expected behavior is established. GenAI can identify the contradiction and propose questions, but it should not independently decide which requirement is correct. Generating official tests for both interpretations can create confusion and may lead to invalid defects. Once the contradiction is resolved, the updated requirement can be supplied to GenAI to generate or refine relevant test conditions. This approach maintains traceability and prevents model-generated assumptions from becoming unintended requirements.

Question 357

A tester uses GenAI to generate test data containing customer names and addresses. Which consideration is most important?

1. Whether the data can be handled according to applicable privacy and organizational policies
2. Whether every name is realistic
3. Whether the generated records are very large
4. Whether the model uses sophisticated terminology

Correct Answer: 1

Explanation

Customer names, addresses, and similar information may constitute personal data and therefore require appropriate protection. The tester should determine whether the data is synthetic, anonymized, or derived from real individuals and ensure that its use complies with organizational policies and applicable privacy requirements. Realistic-looking data is not automatically safe, particularly if it accidentally reproduces actual personal information. GenAI tools should also be assessed according to approved data-handling rules before sensitive information is entered. Synthetic data can reduce exposure, but it should still be reviewed for privacy and suitability before being used in testing.

Question 358

Which approach is most suitable for evaluating the quality of GenAI-generated test cases over time?

1. Use defined metrics and periodically compare results against established criteria
2. Judge quality only by the number of generated cases
3. Assume newer outputs are always better
4. Evaluate only the visual appearance of the test cases

Correct Answer: 1

Explanation

Defined evaluation criteria allow teams to monitor whether GenAI-assisted testing is delivering useful results over time. Possible measures include requirement coverage, defect detection contribution, correctness, duplication, relevance, review effort, and the proportion of outputs requiring significant correction. Metrics should reflect the purpose and risk of the testing activity rather than focusing solely on quantity. Periodic evaluation can reveal changes in output quality, prompting effectiveness, or model behavior. A newer model or a larger number of generated cases does not automatically mean better testing. Meaningful evaluation connects GenAI use to measurable testing objectives and quality expectations.

Question 359

A tester asks GenAI to generate tests using information from an internal confidential document. What should the tester confirm before submitting the document?

1. That the GenAI service and data handling are approved for that information
2. That the document contains many pages
3. That the model can summarize documents
4. That the generated output will be short

Correct Answer: 1

Explanation

Confidential information should only be provided to a GenAI service when its use is authorized and appropriate security and data-handling controls are in place. The tester should understand organizational policies, contractual restrictions, data retention, access controls, and whether the service is approved for confidential information. If the service is not approved, the tester may need to redact sensitive content or use an authorized alternative. Document size or output length does not address confidentiality risk. This control is important because prompts and uploaded documents may contain proprietary information that should not be exposed to unauthorized systems or parties.

Question 360

A tester wants GenAI to generate tests for a feature with a strict audit requirement. Which additional practice is especially valuable?

1. Keep records showing the source requirements, generated artifacts, review decisions, and relevant changes
2. Delete all generated outputs after execution
3. Allow undocumented changes to the generated tests
4. Avoid recording who reviewed the results

Correct Answer: 1

Explanation

Strict audit requirements benefit from clear records of how testing artifacts were created, reviewed, and changed. Relevant evidence can include source requirements, prompts or generation instructions, generated outputs, reviewer decisions, modifications, and applicable model or tool information. These records support traceability and help demonstrate that appropriate human oversight was applied. Deleting generated artifacts or failing to record review decisions makes it harder to reconstruct the testing process. The exact records required depend on organizational and regulatory expectations. GenAI can support efficient test creation, but auditability requires a controlled process in which important decisions and changes remain traceable.