{"id":25088,"date":"2026-09-30T11:56:06","date_gmt":"2026-09-30T11:56:06","guid":{"rendered":"https:\/\/www.examlabs.com\/certification\/?p=25088"},"modified":"2026-09-30T11:56:06","modified_gmt":"2026-09-30T11:56:06","slug":"istqb-ct-genai-practice-test-questions-and-exam-dumps-part15-q281-300","status":"publish","type":"post","link":"https:\/\/www.examlabs.com\/certification\/istqb-ct-genai-practice-test-questions-and-exam-dumps-part15-q281-300\/","title":{"rendered":"ISTQB CT-GenAI Practice Test Questions and Exam Dumps Part15 Q281-300"},"content":{"rendered":"

View Full <\/b>ISTQB CT-GenAI Exam Dumps<\/b><\/a> and Practice Test Dumps.<\/b><\/h2>\n

 <\/p>\n

Question 281<\/b><\/h3>\n

A tester asks GenAI to generate test cases from a feature specification. Which information would most directly improve the relevance of the generated tests?<\/b><\/p>\n

    \n
  1. The tester’s preferred writing style<\/span><\/li>\n
  2. The project logo<\/span><\/li>\n
  3. The feature requirements, constraints, and expected behavior<\/span><\/li>\n
  4. The number of people on the testing team<\/span><\/li>\n<\/ol>\n

    Correct Answer: 3<\/b><\/p>\n

    Explanation<\/b><\/p>\n

    Relevant requirements and constraints provide the model with the information needed to generate meaningful tests. Expected behavior is particularly important because it helps establish what should be verified and what constitutes a successful outcome. Information such as a project logo, team size, or writing preference may affect presentation but does not provide the functional context needed for test design. Even with detailed requirements, the generated tests must be reviewed against the authoritative specification. GenAI can assist with identifying scenarios, but it cannot guarantee complete or correct coverage. Providing focused, accurate context generally improves the relevance and usefulness of generated test cases.<\/span><\/p>\n

    Question 282<\/b><\/h3>\n

    A GenAI tool generates an expected result that is not supported by the approved requirements. What should the tester do?<\/b><\/p>\n

      \n
    1. Verify the expected result against the authoritative requirements before using the test<\/span><\/li>\n
    2. Accept it because the model generated it confidently<\/span><\/li>\n
    3. Change the requirement to match the generated result<\/span><\/li>\n
    4. Execute the test without reviewing the expected result<\/span><\/li>\n<\/ol>\n

      Correct Answer: 1<\/b><\/p>\n

      Explanation<\/b><\/p>\n

      Expected results should be based on authoritative sources rather than assumptions generated by the model. If an expected result is not supported by approved requirements, the tester should investigate the discrepancy and obtain clarification when necessary. Accepting an unsupported result could cause a valid system behavior to be reported as a failure or an actual defect to be missed. Changing requirements simply to match generated content is inappropriate unless an authorized requirements process approves the change. GenAI output should therefore be treated as a draft requiring validation. This approach preserves test accuracy, traceability, and alignment with the intended system behavior.<\/span><\/p>\n

      Question 283<\/b><\/h3>\n

      Which prompt would provide the clearest instruction for generating boundary-value tests for a field that accepts values from 1 to 100?<\/b><\/p>\n

        \n
      1. Generate many tests for the field.<\/span><\/li>\n
      2. Generate random values for the field.<\/span><\/li>\n
      3. Test typical values only.<\/span><\/li>\n
      4. Generate tests for values at and immediately around the lower and upper boundaries.<\/span><\/li>\n<\/ol>\n

        Correct Answer: 4<\/b><\/p>\n

        Explanation<\/b><\/p>\n

        Boundary-value testing focuses on values at or near the limits of an accepted range. For a range from 1 through 100, useful values include the lower and upper boundaries and values immediately inside or outside them, depending on the selected test-design technique. A vague request for many tests or random values does not specifically direct the model toward boundary conditions. Testing only typical values may miss defects associated with validation limits. Explicitly identifying the boundary objective gives GenAI a clear testing strategy to follow. The generated values should still be checked against the actual requirement before being incorporated into the test suite.<\/span><\/p>\n

        Question 284<\/b><\/h3>\n

        A tester wants GenAI to produce test cases in a format that can later be imported into a test-management tool. What should the tester provide?<\/b><\/p>\n

          \n
        1. Only the name of the application<\/span><\/li>\n
        2. The required field names, data types, and output structure<\/span><\/li>\n
        3. A request for a creative response<\/span><\/li>\n
        4. An instruction to ignore formatting<\/span><\/li>\n<\/ol>\n

          Correct Answer: 2<\/b><\/p>\n

          Explanation<\/b><\/p>\n

          When generated output will be consumed by another system, the prompt should clearly define the expected schema. Field names, required values, data types, delimiters, or JSON structure can help make the output more consistent and easier to process. A creative or unstructured response may require substantial manual conversion and can introduce formatting errors. However, even correctly formatted output must be validated for factual accuracy and completeness before import. The tester should also verify that the generated values conform to the test-management tool’s actual requirements. Structured prompting therefore supports automation and consistency without eliminating the need for human review.<\/span><\/p>\n

          Question 285<\/b><\/h3>\n

          A tester uses GenAI to suggest test scenarios for a financial transaction workflow. Which consideration is especially important?<\/b><\/p>\n

            \n
          1. The number of words in each generated scenario<\/span><\/li>\n
          2. Whether the model uses advanced terminology<\/span><\/li>\n
          3. Whether the scenarios reflect approved financial business rules and controls<\/span><\/li>\n
          4. Whether the scenarios contain colorful descriptions<\/span><\/li>\n<\/ol>\n

            Correct Answer: 3<\/b><\/p>\n

            Explanation<\/b><\/p>\n

            Financial workflows often depend on precise business rules, transaction limits, authorization conditions, validation requirements, and other controls. Generated scenarios should therefore be checked carefully against approved financial requirements. A scenario that sounds technically sophisticated may still be incorrect if it violates an established business rule. The tester should provide relevant context and verify expected outcomes using authoritative documentation. GenAI can help identify combinations and edge cases, but financial testing requires careful validation because incorrect assumptions can lead to missed defects or misleading results. The quality of the generated scenarios depends primarily on their alignment with the actual business rules rather than their wording or length.<\/span><\/p>\n

            Question 286<\/b><\/h3>\n

            A tester provides GenAI with several examples of correctly formatted test cases before requesting new ones. What is this an example of?<\/b><\/p>\n

              \n
            1. Zero-shot prompting<\/span><\/li>\n
            2. Few-shot prompting<\/span><\/li>\n
            3. Data masking<\/span><\/li>\n
            4. Regression testing<\/span><\/li>\n<\/ol>\n

              Correct Answer: 2<\/b><\/p>\n

              Explanation<\/b><\/p>\n

              Few-shot prompting involves providing multiple examples that demonstrate the desired task, structure, or response pattern. In this situation, the examples show GenAI how existing test cases are formatted and what type of information they contain. The model can use these examples as guidance when generating additional cases. Zero-shot prompting would provide the task without examples. Data masking is a technique for protecting sensitive information, while regression testing concerns verifying existing functionality after changes. Examples used for few-shot prompting should be relevant and accurate, and sensitive information should not be included unless its use is authorized.<\/span><\/p>\n

              Question 287<\/b><\/h3>\n

              A tester asks GenAI to analyze a failed automated test and suggest possible causes. How should the suggestions be treated?<\/b><\/p>\n

                \n
              1. As confirmed root causes<\/span><\/li>\n
              2. As evidence that the application is defective<\/span><\/li>\n
              3. As hypotheses that should be investigated using actual evidence<\/span><\/li>\n
              4. As replacement results for the failed test<\/span><\/li>\n<\/ol>\n

                Correct Answer: 3<\/b><\/p>\n

                Explanation<\/b><\/p>\n

                GenAI can help brainstorm possible explanations for a failed test, but its suggestions are not automatically confirmed findings. The tester should investigate logs, screenshots, application behavior, environment conditions, recent changes, and other relevant evidence. A generated hypothesis may help guide the investigation but should not be recorded as a confirmed root cause without verification. Treating generated explanations as facts can lead to inaccurate defect reports and wasted effort. This is a useful example of human-in-the-loop testing: GenAI expands possible lines of investigation while the tester uses authoritative evidence to determine what actually happened.<\/span><\/p>\n

                Question 288<\/b><\/h3>\n

                Which practice can help reduce the risk of exposing personally identifiable information when using GenAI for test-data generation?<\/b><\/p>\n

                  \n
                1. Use approved synthetic or appropriately anonymized data instead of unnecessary real personal data<\/span><\/li>\n
                2. Upload complete production databases<\/span><\/li>\n
                3. Include real customer passwords in prompts<\/span><\/li>\n
                4. Disable all data-protection controls<\/span><\/li>\n<\/ol>\n

                  Correct Answer: 1<\/b><\/p>\n

                  Explanation<\/b><\/p>\n

                  Using synthetic or properly anonymized data can reduce exposure of personally identifiable information during GenAI-assisted testing. Real production data may contain names, addresses, account information, identifiers, or other sensitive fields that should not be shared without authorization. Simply assuming that the model will protect the information is insufficient. Organizations should apply their data-classification and privacy requirements and use approved services and techniques. Synthetic data should still be checked for realism and suitability for the testing objective. This approach allows testers to obtain useful data variations while reducing unnecessary exposure of sensitive customer information.<\/span><\/p>\n

                  Question 289<\/b><\/h3>\n

                  A tester notices that GenAI repeatedly omits a specific error condition from generated test cases. What should the tester investigate first?<\/b><\/p>\n

                    \n
                  1. Whether the error condition is clearly represented in the requirements or prompt context<\/span><\/li>\n
                  2. Whether the model should be allowed to invent requirements<\/span><\/li>\n
                  3. Whether all successful cases should be duplicated<\/span><\/li>\n
                  4. Whether the error condition should automatically be removed from testing<\/span><\/li>\n<\/ol>\n

                    Correct Answer: 1<\/b><\/p>\n

                    Explanation<\/b><\/p>\n

                    If an important condition is repeatedly omitted, the tester should first determine whether the model received sufficient information about that condition. The relevant requirement, business rule, risk, or example may be missing or unclear in the prompt. Adding focused context or examples can help improve generation. The tester should not ask the model to invent requirements or simply remove the missing condition from testing. After refinement, the generated output should be compared with the approved specification to determine whether coverage has improved. This illustrates that GenAI output quality depends partly on the quality and completeness of the context provided.<\/span><\/p>\n

                    Question 290<\/b><\/h3>\n

                    A team uses an external GenAI service to process project information. Which factor should be checked before using the service for confidential material?<\/b><\/p>\n

                      \n
                    1. Whether the service is authorized under the organization’s data-handling policy<\/span><\/li>\n
                    2. Whether the service has an attractive user interface<\/span><\/li>\n
                    3. Whether the model produces long answers<\/span><\/li>\n
                    4. Whether other teams use the service for public information<\/span><\/li>\n<\/ol>\n

                      Correct Answer: 1<\/b><\/p>\n

                      Explanation<\/b><\/p>\n

                      Authorization and data-handling requirements are essential when confidential information may be processed by an external GenAI service. The team should determine whether the service is approved, how submitted information is handled, what contractual protections apply, and whether organizational security requirements are satisfied. A convenient interface or widespread use for public information does not establish that confidential project material can safely be submitted. If the service is not authorized, the team should use an approved alternative or appropriately sanitized data. This control helps reduce privacy, confidentiality, intellectual-property, and compliance risks associated with external GenAI processing.<\/span><\/p>\n

                      Question 291<\/b><\/h3>\n

                      A tester asks GenAI to identify duplicate test cases in a large test suite. Which information is most useful for this task?<\/b><\/p>\n

                        \n
                      1. Only the test-case titles<\/span><\/li>\n
                      2. Test objectives, inputs, conditions, actions, and expected results<\/span><\/li>\n
                      3. Only the creation dates<\/span><\/li>\n
                      4. Only the names of the testers<\/span><\/li>\n<\/ol>\n

                        Correct Answer: 2<\/b><\/p>\n

                        Explanation<\/b><\/p>\n

                        Determining whether two test cases are duplicates requires more than comparing their titles. Their objectives, inputs, preconditions, actions, expected results, and relevant coverage should be considered. Two tests with different titles may verify the same behavior, while similarly named tests may exercise different conditions. Providing the substantive test information gives GenAI a stronger basis for identifying potential redundancy. The results should still be reviewed by testers because semantic differences can be subtle and the model may incorrectly classify cases. This type of GenAI assistance can help reduce unnecessary duplication and improve the maintainability of a test suite.<\/span><\/p>\n

                        Question 292<\/b><\/h3>\n

                        A GenAI-generated test includes an assumption about a third-party service that is not documented in the project requirements. What should happen next?<\/b><\/p>\n

                          \n
                        1. The assumption should be verified against current authoritative information<\/span><\/li>\n
                        2. The assumption should automatically become part of the requirements<\/span><\/li>\n
                        3. The test should be approved without review<\/span><\/li>\n
                        4. The third-party service should be replaced<\/span><\/li>\n<\/ol>\n

                          Correct Answer: 1<\/b><\/p>\n

                          Explanation<\/b><\/p>\n

                          Assumptions about external services can become invalid when APIs, policies, configurations, or supported behaviors change. If a generated test contains an undocumented assumption, the tester should verify it using current authoritative documentation or an appropriate service owner. The assumption should not automatically become a requirement or be accepted without evidence. If the assumption is incorrect, the test should be corrected or discarded. GenAI can generate plausible technical details that sound reasonable but are not necessarily supported by the actual service. Verification therefore remains essential when generated tests depend on external systems or integrations.<\/span><\/p>\n

                          Question 293<\/b><\/h3>\n

                          Which characteristic of a GenAI-generated test case most directly supports traceability?<\/b><\/p>\n

                            \n
                          1. The use of detailed technical terminology<\/span><\/li>\n
                          2. A clear reference to the relevant approved requirement or test objective<\/span><\/li>\n
                          3. A long explanation of the test<\/span><\/li>\n
                          4. A generated filename<\/span><\/li>\n<\/ol>\n

                            Correct Answer: 2<\/b><\/p>\n

                            Explanation<\/b><\/p>\n

                            Traceability connects testing artifacts to the requirements, risks, or objectives they are intended to verify. A valid reference to an approved requirement or test objective makes it easier to determine why a test exists and what behavior it covers. Technical terminology and detailed explanations may improve readability but do not establish traceability by themselves. Generated filenames are also not reliable traceability mechanisms. When GenAI creates tests, testers should verify that references correspond to real approved identifiers and that the test actually addresses the referenced requirement. This supports coverage analysis, change impact assessment, reporting, and controlled test maintenance.<\/span><\/p>\n

                            Question 294<\/b><\/h3>\n

                            A tester wants GenAI to generate both positive and negative API tests. Which instruction is most useful?<\/b><\/p>\n

                              \n
                            1. Generate only successful requests<\/span><\/li>\n
                            2. Focus on the API documentation title<\/span><\/li>\n
                            3. Include valid requests, invalid inputs, unauthorized access, and boundary conditions<\/span><\/li>\n
                            4. Avoid requests that return errors<\/span><\/li>\n<\/ol>\n

                              Correct Answer: 3<\/b><\/p>\n

                              Explanation<\/b><\/p>\n

                              A comprehensive API test design should consider both expected successful behavior and conditions in which the service should reject or handle requests appropriately. Invalid inputs, unauthorized access, malformed requests, boundary values, and other negative conditions can reveal validation and security defects. Asking for successful requests only creates an incomplete test perspective. Avoiding errors also prevents the tester from verifying important failure behavior. The generated cases should be compared with the API specification to ensure that status codes, response structures, authentication rules, and validation behavior are accurate. GenAI can expand scenario ideas, but the API contract remains the authoritative basis for expected behavior.<\/span><\/p>\n

                              Question 295<\/b><\/h3>\n

                              A team wants to determine whether GenAI is reducing test-design effort without reducing test quality. Which measurement approach is most appropriate?<\/b><\/p>\n

                                \n
                              1. Measure only the number of prompts submitted<\/span><\/li>\n
                              2. Compare effort and relevant quality outcomes against defined baseline criteria<\/span><\/li>\n
                              3. Count only the number of generated words<\/span><\/li>\n
                              4. Assume quality is unchanged whenever time is saved<\/span><\/li>\n<\/ol>\n

                                Correct Answer: 2<\/b><\/p>\n

                                Explanation<\/b><\/p>\n

                                Evaluating productivity should consider both efficiency and quality. A team can compare test-design effort with an established baseline while also measuring relevant outcomes such as requirement coverage, defect detection, review effort, correctness, or maintenance impact. Saving time alone does not prove that testing quality has been preserved. Counting prompts or generated words provides little evidence about testing effectiveness. A defined evaluation framework helps the organization determine whether GenAI provides practical benefits without introducing unacceptable risks. Measurements should reflect the team’s objectives and be applied consistently so that comparisons are meaningful.<\/span><\/p>\n

                                Question 296<\/b><\/h3>\n

                                A tester asks GenAI to convert a manual test into automation code. The generated script removes an important assertion. What should the tester do?<\/b><\/p>\n

                                  \n
                                1. Accept the script because it executes successfully<\/span><\/li>\n
                                2. Restore the required assertion and review the rest of the generated code<\/span><\/li>\n
                                3. Remove all remaining assertions<\/span><\/li>\n
                                4. Delete the original manual test<\/span><\/li>\n<\/ol>\n

                                  Correct Answer: 2<\/b><\/p>\n

                                  Explanation<\/b><\/p>\n

                                  Assertions are essential because they verify whether the observed behavior matches the expected result. If GenAI removes an important assertion during automation conversion, the generated script no longer fully represents the original test intent. The tester should restore the required assertion and review the rest of the code for similar omissions. Successful execution alone does not demonstrate that the automated test is effective; a script can run without actually verifying the intended behavior. Comparing generated automation with the original manual test helps preserve coverage, expected results, and business intent while taking advantage of GenAI-assisted code generation.<\/span><\/p>\n

                                  Question 297<\/b><\/h3>\n

                                  A tester provides GenAI with a confidential defect report and asks for a summary using an external service that has not been approved by the organization. What is the primary concern?<\/b><\/p>\n

                                    \n
                                  1. The summary may contain too few words<\/span><\/li>\n
                                  2. The model may use a different writing style<\/span><\/li>\n
                                  3. The confidential information may be exposed to an unauthorized service<\/span><\/li>\n
                                  4. The report may become easier to read<\/span><\/li>\n<\/ol>\n

                                    Correct Answer: 3<\/b><\/p>\n

                                    Explanation<\/b><\/p>\n

                                    The primary concern is unauthorized disclosure of confidential information. A defect report may contain customer information, internal architecture details, security findings, credentials, or other sensitive material. Sending it to an unapproved external GenAI service could violate organizational policy or create security, privacy, or contractual risks. The fact that the requested task is only summarization does not remove the data-protection concern. The tester should use an approved service or appropriately sanitize the information before processing it. This example demonstrates that security and privacy considerations apply to GenAI-assisted documentation tasks as well as test generation and automation.<\/span><\/p>\n

                                    Question 298<\/b><\/h3>\n

                                    Which approach is most appropriate for evaluating whether GenAI-generated test cases are complete enough for a specific requirement?<\/b><\/p>\n

                                      \n
                                    1. Compare the cases with the requirement’s stated behaviors, conditions, and acceptance criteria<\/span><\/li>\n
                                    2. Count the number of generated test cases only<\/span><\/li>\n
                                    3. Select the test cases with the longest descriptions<\/span><\/li>\n
                                    4. Ask GenAI whether the cases are complete and accept its response<\/span><\/li>\n<\/ol>\n

                                      Correct Answer: 1<\/b><\/p>\n

                                      Explanation<\/b><\/p>\n

                                      Completeness should be evaluated against the actual requirement and its relevant behaviors, conditions, constraints, and acceptance criteria. A large number of test cases does not necessarily provide complete coverage, because many cases may duplicate the same scenario while missing important conditions. Long descriptions are similarly not evidence of completeness. Asking GenAI to judge its own output can provide an additional perspective but should not replace independent evaluation. Testers should identify which requirement elements need verification and determine whether the generated cases address them. This structured comparison helps reveal missing scenarios and supports more reliable coverage assessment.<\/span><\/p>\n

                                      Question 299<\/b><\/h3>\n

                                      A GenAI tool generates a test-data value that violates a mandatory field constraint in the specification. What should the tester do?<\/b><\/p>\n

                                        \n
                                      1. Treat the generated value as the correct expected input<\/span><\/li>\n
                                      2. Validate the value against the specification and correct or classify it appropriately<\/span><\/li>\n
                                      3. Change the specification to allow the value<\/span><\/li>\n
                                      4. Ignore the constraint<\/span><\/li>\n<\/ol>\n

                                        Correct Answer: 2<\/b><\/p>\n

                                        Explanation<\/b><\/p>\n

                                        Generated test data should be evaluated against the rules defined by the system specification. If a mandatory constraint is violated, the tester should determine whether the value is intended as a negative test or whether it was generated incorrectly. For negative testing, the invalid value may be useful when paired with an expected rejection. For a positive test, the value should be corrected. The specification should not be changed simply because GenAI generated a conflicting value. This illustrates why generated test data requires contextual validation and why the same input can be appropriate or inappropriate depending on the specific testing objective.<\/span><\/p>\n

                                        Question 300<\/b><\/h3>\n

                                        A testing team uses GenAI to assist with test design, automation, and reporting. Which practice best supports responsible use across these activities?<\/b><\/p>\n

                                          \n
                                        1. Allow GenAI to make all final testing decisions<\/span><\/li>\n
                                        2. Use GenAI without documenting how outputs are reviewed<\/span><\/li>\n
                                        3. Define appropriate usage boundaries, validate outputs, and maintain human accountability<\/span><\/li>\n
                                        4. Avoid reviewing generated content when deadlines are tight<\/span><\/li>\n<\/ol>\n

                                          Correct Answer: 3<\/b><\/p>\n

                                          Explanation<\/b><\/p>\n

                                          Responsible GenAI use requires a controlled process covering appropriate use cases, data protection, validation, governance, and human accountability. Teams should define where GenAI can assist, what information may be provided, which outputs require review, and who is responsible for approving generated artifacts. Validation is important because generated content can contain hallucinations, omissions, bias, outdated information, or insecure code. Documentation can also support traceability and continuous improvement. Allowing the model to make final decisions or skipping review under time pressure increases risk. GenAI can provide significant assistance, but responsible use keeps human professionals accountable for important testing outcomes and decisions.<\/span><\/p>\n

                                           <\/p>\n","protected":false},"excerpt":{"rendered":"

                                          View Full ISTQB CT-GenAI Exam Dumps and Practice Test Dumps.   Question 281 A tester asks GenAI to generate test cases from a feature specification. Which information would most directly improve the relevance of the generated tests? The tester’s preferred writing style The project logo The feature requirements, constraints, and expected behavior The number of […]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":[],"categories":[1648,1647],"tags":[],"_links":{"self":[{"href":"https:\/\/www.examlabs.com\/certification\/wp-json\/wp\/v2\/posts\/25088"}],"collection":[{"href":"https:\/\/www.examlabs.com\/certification\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.examlabs.com\/certification\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.examlabs.com\/certification\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/www.examlabs.com\/certification\/wp-json\/wp\/v2\/comments?post=25088"}],"version-history":[{"count":1,"href":"https:\/\/www.examlabs.com\/certification\/wp-json\/wp\/v2\/posts\/25088\/revisions"}],"predecessor-version":[{"id":25089,"href":"https:\/\/www.examlabs.com\/certification\/wp-json\/wp\/v2\/posts\/25088\/revisions\/25089"}],"wp:attachment":[{"href":"https:\/\/www.examlabs.com\/certification\/wp-json\/wp\/v2\/media?parent=25088"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.examlabs.com\/certification\/wp-json\/wp\/v2\/categories?post=25088"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.examlabs.com\/certification\/wp-json\/wp\/v2\/tags?post=25088"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}