{"id":25070,"date":"2026-09-30T11:39:48","date_gmt":"2026-09-30T11:39:48","guid":{"rendered":"https:\/\/www.examlabs.com\/certification\/?p=25070"},"modified":"2026-09-30T11:39:48","modified_gmt":"2026-09-30T11:39:48","slug":"istqb-ct-genai-practice-test-questions-and-exam-dumps-part6-q101-120","status":"publish","type":"post","link":"https:\/\/www.examlabs.com\/certification\/istqb-ct-genai-practice-test-questions-and-exam-dumps-part6-q101-120\/","title":{"rendered":"ISTQB CT-GenAI Practice Test Questions and Exam Dumps Part6 Q101-120"},"content":{"rendered":"

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

 <\/p>\n

Question 101<\/b><\/h3>\n

Which statement best describes the purpose of retrieval-augmented generation (RAG) in a GenAI testing solution?<\/b><\/p>\n

    \n
  1. To replace the underlying language model with a database<\/span><\/li>\n
  2. To execute automated tests without any test environment<\/span><\/li>\n
  3. To provide the model with relevant external information for generating a response<\/span><\/li>\n
  4. To permanently modify the model after every prompt<\/span><\/li>\n<\/ol>\n

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

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

    Retrieval-augmented generation, commonly called RAG, combines a language model with a mechanism for retrieving relevant information from an external knowledge source. In software testing, this can help a GenAI system use current project requirements, test documentation, defect information, or other approved sources when generating responses. The retrieved information provides additional context that may improve relevance and reduce reliance on potentially outdated or incomplete model knowledge. However, retrieval does not guarantee that the generated response is correct. Testers should still verify generated results against authoritative project information and established testing objectives.<\/span><\/p>\n

    Question 102<\/b><\/h3>\n

    A tester wants GenAI to generate test cases only for the password-reset feature and exclude unrelated functionality. Which prompt element is most useful?<\/b><\/p>\n

      \n
    1. Scope constraint<\/span><\/li>\n
    2. Random example<\/span><\/li>\n
    3. Unrelated context<\/span><\/li>\n
    4. Output repetition<\/span><\/li>\n<\/ol>\n

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

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

      A scope constraint clearly defines which part of the system should be considered during generation. In this example, specifying that the model should focus only on the password-reset feature helps prevent unrelated functionality from appearing in the generated test cases. Scope can be further clarified using requirements, user roles, business rules, and expected behaviors. A narrow scope does not guarantee that the generated cases are complete or correct, so they should still be reviewed. Clear scope definition is particularly useful when an application contains many features and the testing task needs to focus on one specific area.<\/span><\/p>\n

      Question 103<\/b><\/h3>\n

      Which risk can result from using a GenAI model that produces outdated technical recommendations?<\/b><\/p>\n

        \n
      1. The model will always refuse to answer technical questions<\/span><\/li>\n
      2. The model may recommend deprecated APIs, libraries, or testing approaches<\/span><\/li>\n
      3. The model will automatically update its knowledge from every project<\/span><\/li>\n
      4. The generated output will always be identical<\/span><\/li>\n<\/ol>\n

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

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

        Software technologies change frequently, and information used by a GenAI model may not always reflect the latest versions, APIs, libraries, or testing practices. As a result, a model can suggest deprecated commands, outdated frameworks, or technical approaches that are no longer appropriate. Testers should verify current technical recommendations using authoritative documentation and project-specific information. Supplying current documentation as context may also improve the usefulness of the response. Outdated information is particularly important to consider when generated content is used to create automation code, configure testing tools, or design technology-specific tests.<\/span><\/p>\n

        Question 104<\/b><\/h3>\n

        What is the main purpose of specifying an output format such as a table with columns for ID, steps, data, and expected result?<\/b><\/p>\n

          \n
        1. To train the model permanently<\/span><\/li>\n
        2. To prevent the model from generating test cases<\/span><\/li>\n
        3. To guarantee that all test cases are correct<\/span><\/li>\n
        4. To make the generated testing information consistently structured<\/span><\/li>\n<\/ol>\n

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

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

          Specifying an output format provides the GenAI system with clear instructions about how the requested information should be organized. A tester may request columns for test ID, objective, preconditions, steps, test data, and expected result. This structure can make generated test cases easier to review, compare, edit, and potentially transfer into testing tools. However, structure only controls presentation and does not guarantee that the content is accurate or complete. Testers must still validate each field against the requirements and testing objectives. Structured output is therefore a useful prompt-engineering technique rather than a substitute for review.<\/span><\/p>\n

          Question 105<\/b><\/h3>\n

          Which activity is an appropriate example of using GenAI to support test planning?<\/b><\/p>\n

            \n
          1. Asking GenAI to suggest testing activities based on provided scope and risks<\/span><\/li>\n
          2. Allowing GenAI to approve the complete test plan without review<\/span><\/li>\n
          3. Removing risk analysis because GenAI generates the plan<\/span><\/li>\n
          4. Allowing the model to determine organizational priorities automatically<\/span><\/li>\n<\/ol>\n

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

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

            GenAI can assist test planning by suggesting testing activities based on information such as project scope, requirements, risks, constraints, environments, and available resources. For example, it may propose functional, integration, regression, usability, or security testing areas for consideration. These suggestions can help testers brainstorm and structure an initial plan. However, the generated plan should be reviewed by people with appropriate project and domain knowledge. Organizational priorities, risks, deadlines, resources, and contractual requirements may require information that the model does not have. Human decision-making therefore remains important.<\/span><\/p>\n

            Question 106<\/b><\/h3>\n

            What should a tester consider when GenAI generates a test case containing a requirement identifier that does not exist?<\/b><\/p>\n

              \n
            1. Accept the identifier because it looks realistic<\/span><\/li>\n
            2. Verify the identifier against the authoritative requirements<\/span><\/li>\n
            3. Create a new requirement automatically<\/span><\/li>\n
            4. Remove all requirement identifiers from the test suite<\/span><\/li>\n<\/ol>\n

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

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

              GenAI can generate plausible-looking identifiers that are not actually present in the project’s requirements. This is another example of why generated information needs validation. The tester should compare the identifier with the authoritative requirements repository or approved documentation. If it does not exist, the test case should be corrected, rejected, or investigated rather than being treated as valid traceability information. Automatically creating a requirement would bypass the organization’s requirements-management process. Accurate traceability depends on linking testing artifacts to genuine and approved requirements.<\/span><\/p>\n

              Question 107<\/b><\/h3>\n

              Which factor can influence the usefulness of a GenAI-generated answer when the prompt contains contradictory requirements?<\/b><\/p>\n

                \n
              1. The model may produce an interpretation that does not match the intended business rule<\/span><\/li>\n
              2. Contradictory requirements always produce perfect test coverage<\/span><\/li>\n
              3. The model automatically resolves conflicts using organizational policy<\/span><\/li>\n
              4. The model cannot generate any response when requirements conflict<\/span><\/li>\n<\/ol>\n

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

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

                Contradictory requirements can create ambiguity for a GenAI model because it may not know which statement represents the authoritative business rule. The model may select one interpretation, combine conflicting information, or produce an answer that does not reflect the intended behavior. Testers should identify such contradictions and seek clarification through the appropriate requirements-management process. GenAI can help highlight possible inconsistencies, but it should not independently decide which requirement is authoritative. Resolving contradictions before relying on generated testing artifacts improves the quality and reliability of subsequent test design.<\/span><\/p>\n

                Question 108<\/b><\/h3>\n

                Which GenAI risk is particularly relevant when confidential test information is entered into an unapproved external service?<\/b><\/p>\n

                  \n
                1. Improved test coverage<\/span><\/li>\n
                2. Faster test execution<\/span><\/li>\n
                3. Potential data leakage or unauthorized disclosure<\/span><\/li>\n
                4. Increased requirements traceability<\/span><\/li>\n<\/ol>\n

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

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

                  Entering confidential test information into an unapproved external GenAI service can create a risk of unauthorized disclosure or data leakage. Test information may include customer details, credentials, proprietary application behavior, security findings, source code, or business-sensitive information. Organizations should define which information can be submitted to GenAI services and which tools are approved for use. Testers should follow data classification, privacy, confidentiality, and security requirements. Technical usefulness does not override these controls. Minimizing sensitive information and using approved environments can help reduce the risk while still allowing GenAI to support testing activities.<\/span><\/p>\n

                  Question 109<\/b><\/h3>\n

                  What is one benefit of asking GenAI to generate multiple test ideas before the tester selects the final cases?<\/b><\/p>\n

                    \n
                  1. It can support brainstorming and broaden the range of scenarios considered<\/span><\/li>\n
                  2. It guarantees complete test coverage<\/span><\/li>\n
                  3. It eliminates the need for risk-based testing<\/span><\/li>\n
                  4. It ensures every generated scenario is executable<\/span><\/li>\n<\/ol>\n

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

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

                    Generating multiple test ideas can help testers brainstorm a wider range of possible scenarios. GenAI may suggest alternative inputs, user behaviors, error conditions, boundaries, or unusual sequences that the tester can evaluate. This can be especially useful during early test analysis when the objective is to explore possibilities before selecting a final set of tests. However, generated ideas may include duplicates, irrelevant cases, or incorrect assumptions. The tester must therefore assess each idea against requirements, risks, feasibility, and testing objectives before incorporating it into the formal test suite.<\/span><\/p>\n

                    Question 110<\/b><\/h3>\n

                    What is a key concern when GenAI is used to generate security-sensitive test scripts?<\/b><\/p>\n

                      \n
                    1. Security-sensitive scripts never require review<\/span><\/li>\n
                    2. Generated code may contain unsafe logic or expose sensitive information<\/span><\/li>\n
                    3. Security testing does not involve code<\/span><\/li>\n
                    4. GenAI automatically applies every organizational security control<\/span><\/li>\n<\/ol>\n

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

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

                      Security-sensitive test scripts can interact with credentials, protected systems, APIs, databases, and other sensitive resources. GenAI-generated scripts may contain insecure handling of credentials, unsafe commands, excessive permissions, or inappropriate data processing. Testers should therefore review generated scripts carefully and validate them in a controlled environment. Security requirements, coding standards, and organizational policies should be applied before the scripts are approved. Generated code can accelerate development, but it should not be trusted automatically. Human review is particularly important when the generated automation could affect sensitive environments.<\/span><\/p>\n

                      Question 111<\/b><\/h3>\n

                      Which approach can help a tester determine whether a GenAI-generated test case is redundant?<\/b><\/p>\n

                        \n
                      1. Compare it with existing tests and identify overlapping objectives and conditions<\/span><\/li>\n
                      2. Accept every generated case to maximize quantity<\/span><\/li>\n
                      3. Delete all manually created tests<\/span><\/li>\n
                      4. Evaluate only the length of the test case<\/span><\/li>\n<\/ol>\n

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

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

                        Redundancy can be assessed by comparing a generated test case with existing tests and determining whether they cover substantially the same objective, conditions, behavior, or expected result. Removing unnecessary duplication can make a test suite easier to maintain and execute. However, apparently similar tests may still provide different coverage because of distinct data, user roles, environments, or risks. Testers should therefore evaluate the purpose and coverage of each case rather than removing cases solely because their wording is similar. GenAI can help identify possible duplicates, but human validation remains important.<\/span><\/p>\n

                        Question 112<\/b><\/h3>\n

                        Which statement describes one possible use of GenAI for test automation maintenance?<\/b><\/p>\n

                          \n
                        1. Automatically approving every changed script<\/span><\/li>\n
                        2. Replacing the automation framework without analysis<\/span><\/li>\n
                        3. Suggesting updated locators or test steps when the user interface changes<\/span><\/li>\n
                        4. Guaranteeing that all changed tests remain valid<\/span><\/li>\n<\/ol>\n

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

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

                          Changes to a user interface can cause automated tests to fail because locators, labels, controls, or workflows may have changed. GenAI can assist by analyzing existing automation and suggesting updated locators or test steps based on the new interface information. Such suggestions should be reviewed and executed before being accepted because the model may choose an incorrect element or misunderstand the intended workflow. GenAI can therefore reduce maintenance effort while leaving validation to the tester or automation engineer. Automated test maintenance still requires regression testing and appropriate quality controls.<\/span><\/p>\n

                          Question 113<\/b><\/h3>\n

                          What does one-shot prompting generally involve?<\/b><\/p>\n

                            \n
                          1. Providing one example to demonstrate the expected task or output<\/span><\/li>\n
                          2. Providing no instructions or examples<\/span><\/li>\n
                          3. Providing hundreds of examples for model training<\/span><\/li>\n
                          4. Asking the model to execute one automated test<\/span><\/li>\n<\/ol>\n

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

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

                            One-shot prompting provides a single example that demonstrates the expected relationship between an input and an output. In testing, a tester might provide one requirement together with a correctly structured test case and then ask the model to create additional cases using the same style. This can clarify expected terminology and output structure compared with instructions alone. One-shot prompting does not retrain the underlying model and does not guarantee correctness. The resulting cases should still be reviewed against the requirements, test objectives, and applicable testing standards.<\/span><\/p>\n

                            Question 114<\/b><\/h3>\n

                            Which practice is useful when evaluating the consistency of GenAI-generated test cases?<\/b><\/p>\n

                              \n
                            1. Checking whether similar inputs produce appropriately consistent structures and interpretations<\/span><\/li>\n
                            2. Accepting every variation as correct<\/span><\/li>\n
                            3. Measuring only the response generation time<\/span><\/li>\n
                            4. Removing all examples from the prompts<\/span><\/li>\n<\/ol>\n

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

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

                              Consistency evaluation can help determine whether GenAI produces appropriately similar structures and interpretations when given comparable testing tasks. A team may use standardized prompts and representative examples, then review whether the generated cases follow expected terminology, formatting, scope, and testing logic. Some variation is normal because GenAI can be probabilistic, but significant inconsistencies may reduce usefulness or reproducibility. Evaluation should focus on meaningful quality characteristics rather than demanding identical wording in every response. Human review and defined quality criteria can help determine whether the observed variation is acceptable.<\/span><\/p>\n

                              Question 115<\/b><\/h3>\n

                              A tester asks GenAI to summarize a 200-page requirements document but the model receives only selected sections. What should the tester consider?<\/b><\/p>\n

                                \n
                              1. The summary may not represent requirements that were not provided to the model<\/span><\/li>\n
                              2. The model automatically reads the complete document anyway<\/span><\/li>\n
                              3. Missing sections are always inferred correctly<\/span><\/li>\n
                              4. The resulting summary is guaranteed to be complete<\/span><\/li>\n<\/ol>\n

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

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

                                A GenAI model can only base its response on the information available to it and any other information accessible through the specific solution. If only selected sections of a large requirements document are provided, the generated summary may omit requirements contained elsewhere. Testers should therefore understand the scope of the supplied context and avoid treating a partial-input summary as a complete representation of the entire document. For important testing activities, the relevant requirements should be systematically covered and the resulting summary should be validated against the authoritative source.<\/span><\/p>\n

                                Question 116<\/b><\/h3>\n

                                Which factor should be considered when deciding whether to automate a testing task with GenAI assistance?<\/b><\/p>\n

                                  \n
                                1. Whether GenAI is currently popular<\/span><\/li>\n
                                2. Whether the task benefits from automation and the associated risks can be controlled<\/span><\/li>\n
                                3. Whether the generated response contains many words<\/span><\/li>\n
                                4. Whether the task requires no validation<\/span><\/li>\n<\/ol>\n

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

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

                                  A testing task should be considered for GenAI assistance based on its potential value, feasibility, and associated risks. Suitable tasks may be repetitive, text-heavy, or suitable for generating initial drafts and suggestions. The team should also consider privacy, security, correctness, review effort, reproducibility, and the impact of errors. A task is not automatically suitable simply because GenAI can perform it. The objective should be to achieve useful productivity or quality improvements while maintaining appropriate controls. Human review may be especially important when incorrect output could affect significant testing decisions.<\/span><\/p>\n

                                  Question 117<\/b><\/h3>\n

                                  What is a potential consequence of providing biased examples in a few-shot prompt?<\/b><\/p>\n

                                    \n
                                  1. The generated output may reproduce or reinforce the bias represented by those examples<\/span><\/li>\n
                                  2. The model automatically removes the bias<\/span><\/li>\n
                                  3. The examples have no influence on generated output<\/span><\/li>\n
                                  4. The model permanently changes its training dataset<\/span><\/li>\n<\/ol>\n

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

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

                                    Examples supplied in a few-shot prompt influence how the model interprets the requested task and expected output. If those examples consistently contain an unwanted bias, the generated results may reproduce or reinforce similar patterns. Testers should therefore select examples carefully and consider whether they represent the intended range of users, scenarios, data, and behaviors. Human review can help identify problematic patterns in generated results. Few-shot examples are useful for improving consistency and clarifying expectations, but they should themselves be reviewed for relevance, accuracy, and unintended bias.<\/span><\/p>\n

                                    Question 118<\/b><\/h3>\n

                                    Which statement about GenAI-generated explanations of test failures is most appropriate?<\/b><\/p>\n

                                      \n
                                    1. They should be considered confirmed root causes<\/span><\/li>\n
                                    2. They are always based on actual system execution evidence<\/span><\/li>\n
                                    3. They can provide hypotheses that require verification against evidence<\/span><\/li>\n
                                    4. They eliminate the need to reproduce the failure<\/span><\/li>\n<\/ol>\n

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

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

                                      GenAI can analyze supplied failure information and generate possible explanations, but these explanations should normally be treated as hypotheses rather than confirmed root causes. The model may lack important information about the environment, configuration, source code, dependencies, or actual execution sequence. Testers should verify suggested causes using logs, reproduction steps, source code, requirements, and other relevant evidence. This approach allows GenAI to accelerate investigation without replacing evidence-based defect analysis. A plausible explanation is useful as a starting point, but it should not be documented as a confirmed cause until appropriately validated.<\/span><\/p>\n

                                      Question 119<\/b><\/h3>\n

                                      Which characteristic of a prompt is most likely to help GenAI distinguish between functional and non-functional testing requests?<\/b><\/p>\n

                                        \n
                                      1. Clear task instructions and explicit testing scope<\/span><\/li>\n
                                      2. Removing all testing terminology<\/span><\/li>\n
                                      3. Providing only the application’s name<\/span><\/li>\n
                                      4. Asking the model to decide the scope without context<\/span><\/li>\n<\/ol>\n

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

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

                                        Clear task instructions and explicit scope help GenAI understand what type of testing activity is being requested. For example, a prompt can state whether the tester wants functional scenarios, performance considerations, usability checks, security scenarios, or another testing focus. Additional requirements and constraints can further clarify the intended output. Without this information, the model may generate a mixture of testing ideas that does not match the tester’s objective. Clear prompting improves relevance, but the generated results still require review to ensure that they address the intended testing scope accurately.<\/span><\/p>\n

                                        Question 120<\/b><\/h3>\n

                                        Which practice best supports accountability when GenAI contributes to an important testing decision?<\/b><\/p>\n

                                          \n
                                        1. Keeping no record of how the decision was made<\/span><\/li>\n
                                        2. Allowing the model to make the final decision independently<\/span><\/li>\n
                                        3. Recording relevant inputs and ensuring an appropriate human reviews and approves the result<\/span><\/li>\n
                                        4. Treating the model’s response as formal evidence without validation<\/span><\/li>\n<\/ol>\n

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

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

                                          Accountability is strengthened when important GenAI-supported decisions have appropriate human ownership and supporting records. Relevant information may include the prompt, source material, generated output, validation activities, and the identity or role of the person responsible for the final decision, depending on organizational processes. This helps explain how the decision was reached and allows issues to be investigated later. GenAI should not independently become the accountable decision-maker for significant testing outcomes. Human review, documented evidence, and established approval processes help ensure that AI assistance remains controlled and traceable.<\/span><\/p>\n

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

                                          View Full ISTQB CT-GenAI Exam Dumps and Practice Test Dumps.   Question 101 Which statement best describes the purpose of retrieval-augmented generation (RAG) in a GenAI testing solution? To replace the underlying language model with a database To execute automated tests without any test environment To provide the model with relevant external information for generating […]<\/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\/25070"}],"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=25070"}],"version-history":[{"count":1,"href":"https:\/\/www.examlabs.com\/certification\/wp-json\/wp\/v2\/posts\/25070\/revisions"}],"predecessor-version":[{"id":25071,"href":"https:\/\/www.examlabs.com\/certification\/wp-json\/wp\/v2\/posts\/25070\/revisions\/25071"}],"wp:attachment":[{"href":"https:\/\/www.examlabs.com\/certification\/wp-json\/wp\/v2\/media?parent=25070"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.examlabs.com\/certification\/wp-json\/wp\/v2\/categories?post=25070"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.examlabs.com\/certification\/wp-json\/wp\/v2\/tags?post=25070"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}