{"id":25060,"date":"2026-09-30T11:30:42","date_gmt":"2026-09-30T11:30:42","guid":{"rendered":"https:\/\/www.examlabs.com\/certification\/?p=25060"},"modified":"2026-09-30T11:30:42","modified_gmt":"2026-09-30T11:30:42","slug":"istqb-ct-genai-practice-test-questions-and-exam-dumps-part1-q1-20","status":"publish","type":"post","link":"https:\/\/www.examlabs.com\/certification\/istqb-ct-genai-practice-test-questions-and-exam-dumps-part1-q1-20\/","title":{"rendered":"ISTQB CT-GenAI Practice Test Questions and Exam Dumps Part1 Q1-20"},"content":{"rendered":"

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

 <\/p>\n

Question 1<\/b><\/h3>\n

What is the primary purpose of generative artificial intelligence (GenAI) in software testing?<\/b><\/p>\n

    \n
  1. To replace all human testers in the software development lifecycle<\/span><\/li>\n
  2. To eliminate the need for software requirements<\/span><\/li>\n
  3. To support testing activities by generating or transforming relevant testing artifacts and information<\/span><\/li>\n
  4. To guarantee that software contains no defects<\/span><\/li>\n<\/ol>\n

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

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

    Generative AI can support software testing by producing useful testing artifacts and information based on provided context and instructions. Examples include generating test ideas, test cases, test data, test scripts, summaries, and defect descriptions. However, GenAI does not guarantee defect-free software and should not automatically replace human judgment. Testers remain responsible for evaluating generated content, verifying its accuracy, and determining whether it adequately addresses testing objectives. The effectiveness of GenAI depends on factors such as the quality of the input, the selected model, available context, and human review. Therefore, GenAI is best viewed as a supporting capability within the testing process.<\/span><\/p>\n

    Question 2<\/b><\/h3>\n

    Which characteristic of GenAI models can cause the model to produce different responses when given similar prompts?<\/b><\/p>\n

      \n
    1. Probabilistic generation<\/span><\/li>\n
    2. Requirement traceability<\/span><\/li>\n
    3. Static compilation<\/span><\/li>\n
    4. Deterministic execution<\/span><\/li>\n<\/ol>\n

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

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

      GenAI models generally use probabilistic mechanisms to generate responses. As a result, similar prompts can sometimes produce different outputs, particularly when generation settings allow variation. This behavior differs from deterministic software functions, where the same input normally produces the same output under identical conditions. For software testing, probabilistic behavior means that testers should not assume that a generated answer is always reproducible or consistently correct. Important generated artifacts should therefore be reviewed and validated. Understanding this characteristic is particularly important when using GenAI for test design, test data generation, defect analysis, or other activities where accuracy and consistency are important.<\/span><\/p>\n

      Question 3<\/b><\/h3>\n

      A tester provides a GenAI model with a role, task, context, constraints, and expected output format. What is this collection primarily intended to improve?<\/b><\/p>\n

        \n
      1. Source-code compilation<\/span><\/li>\n
      2. Prompt effectiveness<\/span><\/li>\n
      3. Network performance<\/span><\/li>\n
      4. Database normalization<\/span><\/li>\n<\/ol>\n

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

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

        Providing a clear role, task, context, constraints, and desired output format can improve prompt effectiveness. A well-structured prompt gives the GenAI model more relevant information about what the tester wants to accomplish. For example, a tester may specify that the model should act as a test analyst, provide requirements as context, request boundary-value test ideas, and require a structured table as output. Better prompts can reduce ambiguity and make generated results more useful. However, even a well-designed prompt does not guarantee correctness. Generated content should still be reviewed, validated, and adjusted by a qualified tester.<\/span><\/p>\n

        Question 4<\/b><\/h3>\n

        Which statement best describes hallucination in the context of GenAI?<\/b><\/p>\n

          \n
        1. The model refusing to generate any output<\/span><\/li>\n
        2. The model generating a response that is factually incorrect but presented as if it were valid<\/span><\/li>\n
        3. The model producing output in a requested format<\/span><\/li>\n
        4. The model using information supplied in the prompt<\/span><\/li>\n<\/ol>\n

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

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

          A hallucination occurs when a GenAI system generates information that appears plausible but is incorrect, unsupported, or fabricated. The response may be expressed confidently even though the underlying information is not reliable. In software testing, hallucinations can create risks if testers accept generated test cases, requirements interpretations, defect explanations, or code without verification. Testers should therefore validate important GenAI outputs against authoritative requirements, source code, documentation, or other reliable evidence. Prompt quality can help reduce some errors, but it cannot completely eliminate hallucinations. Human review remains an important safeguard when GenAI is used in testing activities.<\/span><\/p>\n

          Question 5<\/b><\/h3>\n

          A tester asks a GenAI tool to create test cases from a requirement. What should the tester do before using the generated test cases?<\/b><\/p>\n

            \n
          1. Accept them automatically because the model generated them<\/span><\/li>\n
          2. Delete all manually created test cases<\/span><\/li>\n
          3. Execute them without reviewing their relevance<\/span><\/li>\n
          4. Review and validate them against the requirement and testing objectives<\/span><\/li>\n<\/ol>\n

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

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

            Generated test cases should be reviewed and validated before they are incorporated into the testing process. GenAI may misunderstand requirements, omit important scenarios, create redundant tests, or introduce incorrect assumptions. The tester should compare generated cases with the original requirements, identify missing coverage, verify expected results, and assess whether the tests address appropriate risks. Human expertise remains important because GenAI does not inherently understand the complete business context or organizational priorities. Automatic acceptance can therefore introduce defects into the testing process itself. Validation ensures that generated test cases are relevant, accurate, and aligned with the intended testing objectives.<\/span><\/p>\n

            Question 6<\/b><\/h3>\n

            Which testing activity can GenAI commonly support by generating multiple realistic variations of input information?<\/b><\/p>\n

              \n
            1. Test data generation<\/span><\/li>\n
            2. Production deployment approval<\/span><\/li>\n
            3. Organizational budgeting<\/span><\/li>\n
            4. Hardware installation<\/span><\/li>\n<\/ol>\n

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

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

              GenAI can assist with test data generation by creating variations of input values based on specified requirements and constraints. For example, a tester can request valid, invalid, boundary, unusual, or representative data for a particular field or business scenario. Generated data can help expand test coverage and reduce manual effort. However, testers must ensure that generated data is appropriate, syntactically valid, and compliant with security and privacy requirements. Sensitive production information should not be exposed to a GenAI system without appropriate authorization and controls. GenAI-generated test data should therefore be reviewed before being used in actual testing.<\/span><\/p>\n

              Question 7<\/b><\/h3>\n

              Which factor is most important when deciding whether sensitive testing information should be entered into an external GenAI service?<\/b><\/p>\n

                \n
              1. The length of the prompt<\/span><\/li>\n
              2. The number of test cases already written<\/span><\/li>\n
              3. Data privacy and security requirements<\/span><\/li>\n
              4. The programming language used by the application<\/span><\/li>\n<\/ol>\n

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

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

                Data privacy and security requirements are critical when deciding whether testing information can be submitted to an external GenAI service. Test artifacts may contain confidential requirements, personal information, credentials, proprietary source code, customer information, or security-sensitive details. Organizations should understand how the service handles submitted information, including storage, retention, access, and potential model-training practices. Appropriate organizational policies and contractual requirements should also be considered. Testers should avoid exposing sensitive information unless the use is authorized and appropriate safeguards are in place. Security and privacy considerations therefore take priority over convenience when selecting GenAI services for testing.<\/span><\/p>\n

                Question 8<\/b><\/h3>\n

                A tester asks a GenAI system to generate test cases but provides no requirements, business rules, or application context. What is the most likely consequence?<\/b><\/p>\n

                  \n
                1. The model will automatically discover all business rules<\/span><\/li>\n
                2. The generated tests may be generic or poorly aligned with the actual system<\/span><\/li>\n
                3. The model will guarantee complete test coverage<\/span><\/li>\n
                4. The generated tests will always match production behavior<\/span><\/li>\n<\/ol>\n

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

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

                  GenAI needs sufficient context to generate useful testing artifacts. Without requirements, business rules, system behavior, or other relevant information, the model may rely on general patterns rather than the specific characteristics of the application. The resulting test cases may therefore be generic, incomplete, irrelevant, or based on assumptions that do not apply to the system. Providing meaningful context can improve the relevance of generated results. Nevertheless, additional context does not guarantee complete coverage or correctness. Testers should evaluate the generated tests against requirements, risks, and expected system behavior before using them as part of a testing strategy.<\/span><\/p>\n

                  Question 9<\/b><\/h3>\n

                  Which approach can help a tester obtain more consistent and useful results from a GenAI model?<\/b><\/p>\n

                    \n
                  1. Use increasingly ambiguous instructions<\/span><\/li>\n
                  2. Remove all context from the prompt<\/span><\/li>\n
                  3. Provide clear instructions, relevant context, constraints, and an expected output structure<\/span><\/li>\n
                  4. Ask unrelated questions in the same prompt without clarification<\/span><\/li>\n<\/ol>\n

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

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

                    Clear instructions, relevant context, constraints, and an expected output structure can improve the usefulness and consistency of GenAI responses. For example, a tester can specify the application area, testing objective, required test design technique, assumptions, and desired format. This gives the model a clearer understanding of the task. Ambiguous prompts can result in broader or less predictable responses because the model must make more assumptions. Even with carefully designed prompts, however, generated content can contain errors or omissions. Testers should therefore combine effective prompting with human review, validation, and appropriate testing expertise.<\/span><\/p>\n

                    Question 10<\/b><\/h3>\n

                    What is one potential benefit of using GenAI to generate software test cases?<\/b><\/p>\n

                      \n
                    1. It can reduce the time required to create initial test ideas<\/span><\/li>\n
                    2. It guarantees that every requirement is covered<\/span><\/li>\n
                    3. It eliminates the need for test design techniques<\/span><\/li>\n
                    4. It proves that the application is defect-free<\/span><\/li>\n<\/ol>\n

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

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

                      One potential benefit of GenAI is that it can accelerate the creation of initial test ideas and testing artifacts. A tester can provide requirements or other context and ask the model to suggest scenarios, test conditions, or candidate test cases. This can reduce manual effort during early test design activities. However, generated test cases do not guarantee complete requirements coverage, correct application behavior, or defect detection. Testers still need to review the results, identify gaps, apply appropriate test design techniques, and adapt the generated content to the specific application and risk profile. GenAI therefore supports productivity rather than replacing professional testing judgment.<\/span><\/p>\n

                      Question 11<\/b><\/h3>\n

                      Why should testers be cautious when using GenAI-generated explanations of software defects?<\/b><\/p>\n

                        \n
                      1. GenAI cannot generate text<\/span><\/li>\n
                      2. GenAI may infer an incorrect root cause from incomplete or misleading information<\/span><\/li>\n
                      3. Defects never require investigation<\/span><\/li>\n
                      4. Generated explanations are always identical<\/span><\/li>\n<\/ol>\n

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

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

                        GenAI may generate a plausible explanation of a defect even when the available evidence is incomplete or misleading. It can infer relationships that are not actually present in the application or incorrectly identify a likely root cause. This is especially important when analyzing complex failures involving multiple components, dependencies, or environmental conditions. Testers should compare generated explanations with logs, source code, reproduction steps, configuration information, and other available evidence. GenAI can help organize information and suggest hypotheses, but those hypotheses should not automatically be treated as confirmed findings. Human investigation remains necessary before assigning a definitive root cause.<\/span><\/p>\n

                        Question 12<\/b><\/h3>\n

                        Which practice is most appropriate when a GenAI model generates test automation code?<\/b><\/p>\n

                          \n
                        1. Execute the code immediately in production<\/span><\/li>\n
                        2. Trust the code because it was generated from a technical prompt<\/span><\/li>\n
                        3. Review, test, and validate the generated code before use<\/span><\/li>\n
                        4. Remove all existing automation scripts<\/span><\/li>\n<\/ol>\n

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

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

                          GenAI-generated test automation code should be reviewed, tested, and validated before being integrated into a testing environment. Generated code may contain syntax errors, incorrect assumptions about frameworks, inappropriate locators, security weaknesses, inefficient logic, or incorrect expected results. The tester should verify that the code follows project standards and accurately implements the intended test. Running unreviewed generated code in production can introduce unnecessary risks. GenAI can accelerate coding tasks, but software generated by an AI system remains subject to the same quality, security, maintainability, and correctness requirements as manually written code.<\/span><\/p>\n

                          Question 13<\/b><\/h3>\n

                          Which concept describes providing examples of desired inputs and outputs in a prompt to guide a GenAI model?<\/b><\/p>\n

                            \n
                          1. Few-shot prompting<\/span><\/li>\n
                          2. Regression testing<\/span><\/li>\n
                          3. Exploratory testing<\/span><\/li>\n
                          4. Mutation testing<\/span><\/li>\n<\/ol>\n

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

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

                            Few-shot prompting involves providing a small number of examples that demonstrate the expected relationship between inputs and outputs. These examples can help guide a GenAI model toward the desired response pattern. For example, a tester may provide several requirements together with corresponding test cases and then ask the model to generate additional cases in the same style. This differs from zero-shot prompting, where no examples are provided. Few-shot prompting can improve output structure and task understanding, but it does not guarantee factual correctness. Generated results should still be reviewed against requirements and testing objectives.<\/span><\/p>\n

                            Question 14<\/b><\/h3>\n

                            A tester asks a GenAI model to identify security vulnerabilities in source code. What is an important limitation to consider?<\/b><\/p>\n

                              \n
                            1. The model may miss vulnerabilities or incorrectly identify non-vulnerable code as vulnerable<\/span><\/li>\n
                            2. The model can mathematically prove that the code is secure<\/span><\/li>\n
                            3. The model automatically performs penetration testing on every system<\/span><\/li>\n
                            4. The model guarantees complete security coverage<\/span><\/li>\n<\/ol>\n

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

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

                              GenAI can assist with identifying potential security issues, but it cannot guarantee complete vulnerability detection or prove that software is secure. The model may overlook vulnerabilities, misunderstand application context, or incorrectly flag safe code as problematic. Security testing should therefore use appropriate specialized tools, techniques, and human expertise in addition to GenAI assistance. Testers should validate suspected vulnerabilities and investigate their actual impact and exploitability. GenAI-generated security findings should be treated as potential observations or hypotheses until verified. This approach reduces the risk of false confidence and helps ensure that important security weaknesses are investigated appropriately.<\/span><\/p>\n

                              Question 15<\/b><\/h3>\n

                              Which factor can influence the quality of output generated by a GenAI model for testing purposes?<\/b><\/p>\n

                                \n
                              1. Only the tester’s job title<\/span><\/li>\n
                              2. The quality and relevance of the information provided to the model<\/span><\/li>\n
                              3. The number of computers in the test lab<\/span><\/li>\n
                              4. The physical size of the application server<\/span><\/li>\n<\/ol>\n

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

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

                                The quality and relevance of information supplied to a GenAI model can significantly influence the usefulness of its output. Relevant requirements, constraints, examples, domain information, expected behavior, and other context can help the model produce more appropriate testing artifacts. Poor, incomplete, outdated, or misleading information can result in similarly problematic outputs. Other factors can also influence results, including the model, prompt structure, generation settings, and task complexity. Testers should therefore provide accurate and relevant context whenever possible. Even high-quality input does not eliminate the need for validation because GenAI outputs can still contain inaccuracies or omissions.<\/span><\/p>\n

                                Question 16<\/b><\/h3>\n

                                What should a tester do if a GenAI-generated test case conflicts with an approved software requirement?<\/b><\/p>\n

                                  \n
                                1. Use the generated test case because AI is more current<\/span><\/li>\n
                                2. Ignore the approved requirement<\/span><\/li>\n
                                3. Validate the conflict and use the authoritative requirement as the basis for the test<\/span><\/li>\n
                                4. Delete the entire test suite<\/span><\/li>\n<\/ol>\n

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

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

                                  Approved requirements are authoritative sources for determining expected system behavior unless they are formally changed through the appropriate process. If a GenAI-generated test case conflicts with an approved requirement, the tester should investigate the discrepancy rather than automatically accepting the AI output. The generated test may be based on an incorrect assumption or misunderstood context. The tester should validate the requirement, clarify any ambiguity with the appropriate stakeholders, and update the test case if necessary. This illustrates an important principle of using GenAI in testing: generated content is an aid and should be evaluated against reliable project information.<\/span><\/p>\n

                                  Question 17<\/b><\/h3>\n

                                  Which risk may arise when testers become overly dependent on GenAI-generated testing suggestions?<\/b><\/p>\n

                                    \n
                                  1. Increased need for human review<\/span><\/li>\n
                                  2. Reduced critical thinking and insufficient independent analysis<\/span><\/li>\n
                                  3. Improved understanding of requirements<\/span><\/li>\n
                                  4. Better traceability automatically<\/span><\/li>\n<\/ol>\n

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

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

                                    Over-reliance on GenAI can create a risk that testers reduce their own critical thinking and accept generated suggestions without sufficient evaluation. GenAI may produce plausible but incomplete or incorrect outputs, so testers need to maintain independent judgment. Excessive dependence can also cause teams to overlook domain-specific risks, unusual scenarios, or requirements that the model did not recognize. Human expertise remains important for interpreting business context, assessing risk, designing effective tests, and determining whether generated artifacts are appropriate. GenAI should therefore augment testing expertise rather than replace the tester’s responsibility for analysis and decision-making.<\/span><\/p>\n

                                    Question 18<\/b><\/h3>\n

                                    Which type of testing artifact can GenAI potentially help summarize from large amounts of testing information?<\/b><\/p>\n

                                      \n
                                    1. Test execution results and defect information<\/span><\/li>\n
                                    2. Physical network cables<\/span><\/li>\n
                                    3. Computer power supplies<\/span><\/li>\n
                                    4. Office furniture inventories<\/span><\/li>\n<\/ol>\n

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

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

                                      GenAI can assist in summarizing large amounts of textual testing information, such as test execution results, defect reports, logs, requirements, or testing notes, when the relevant information is provided appropriately. Summarization can help testers identify important trends, recurring issues, outstanding defects, and key observations more efficiently. However, summaries may omit details or misinterpret information, especially when the input is complex or ambiguous. Testers should therefore verify important conclusions against the original evidence. GenAI is particularly useful for reducing the manual effort involved in processing large quantities of information, but human review remains important for consequential decisions.<\/span><\/p>\n

                                      Question 19<\/b><\/h3>\n

                                      Which statement best describes the role of a human tester when using GenAI for software testing?<\/b><\/p>\n

                                        \n
                                      1. The tester should accept all generated content without review<\/span><\/li>\n
                                      2. The tester is responsible only for writing prompts<\/span><\/li>\n
                                      3. The tester should validate, interpret, and make appropriate decisions about generated outputs<\/span><\/li>\n
                                      4. The tester is no longer responsible for testing quality<\/span><\/li>\n<\/ol>\n

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

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

                                        Human testers remain responsible for evaluating and appropriately using GenAI-generated outputs. This includes reviewing accuracy, relevance, completeness, risks, and alignment with requirements and testing objectives. Testers also provide domain knowledge and judgment that GenAI may not possess. Writing effective prompts is useful, but the tester’s role extends beyond prompt creation. Automatically accepting generated content can introduce errors into test cases, automation, defect analysis, or test reporting. GenAI should therefore be treated as a supporting tool that can increase productivity and generate ideas while the tester maintains responsibility for validation, interpretation, risk assessment, and testing decisions.<\/span><\/p>\n

                                        Question 20<\/b><\/h3>\n

                                        What is an important consideration when introducing GenAI into an organization’s software testing process?<\/b><\/p>\n

                                          \n
                                        1. Establishing appropriate governance, security, privacy, and usage guidelines<\/span><\/li>\n
                                        2. Allowing unrestricted access to all confidential information<\/span><\/li>\n
                                        3. Removing all existing testing procedures<\/span><\/li>\n
                                        4. Assuming that generated outputs require no verification<\/span><\/li>\n<\/ol>\n

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

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

                                          Introducing GenAI into software testing requires appropriate governance and controls. Organizations should define acceptable use, data handling requirements, privacy protections, security controls, review responsibilities, and expectations for validating generated content. Existing testing procedures should not automatically be removed because GenAI output can contain errors, omissions, or inappropriate assumptions. Access to confidential information should also be controlled according to organizational policies and applicable requirements. Clear guidelines help testers use GenAI responsibly while managing associated risks. Governance should address not only technical capabilities but also human oversight, accountability, data protection, and the quality assurance of generated testing artifacts.<\/span><\/p>\n

                                          ISTQB CT-GenAI Practice Test Questions and Exam Dumps Part1 Q1-20<\/b><\/h1>\n

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

                                           <\/p>\n

                                          Question 1<\/b><\/h3>\n

                                          What is the primary purpose of generative artificial intelligence (GenAI) in software testing?<\/b><\/p>\n

                                            \n
                                          1. To replace all human testers in the software development lifecycle<\/span><\/li>\n
                                          2. To eliminate the need for software requirements<\/span><\/li>\n
                                          3. To support testing activities by generating or transforming relevant testing artifacts and information<\/span><\/li>\n
                                          4. To guarantee that software contains no defects<\/span><\/li>\n<\/ol>\n

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

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

                                            Generative AI can support software testing by producing useful testing artifacts and information based on provided context and instructions. Examples include generating test ideas, test cases, test data, test scripts, summaries, and defect descriptions. However, GenAI does not guarantee defect-free software and should not automatically replace human judgment. Testers remain responsible for evaluating generated content, verifying its accuracy, and determining whether it adequately addresses testing objectives. The effectiveness of GenAI depends on factors such as the quality of the input, the selected model, available context, and human review. Therefore, GenAI is best viewed as a supporting capability within the testing process.<\/span><\/p>\n

                                            Question 2<\/b><\/h3>\n

                                            Which characteristic of GenAI models can cause the model to produce different responses when given similar prompts?<\/b><\/p>\n

                                              \n
                                            1. Probabilistic generation<\/span><\/li>\n
                                            2. Requirement traceability<\/span><\/li>\n
                                            3. Static compilation<\/span><\/li>\n
                                            4. Deterministic execution<\/span><\/li>\n<\/ol>\n

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

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

                                              GenAI models generally use probabilistic mechanisms to generate responses. As a result, similar prompts can sometimes produce different outputs, particularly when generation settings allow variation. This behavior differs from deterministic software functions, where the same input normally produces the same output under identical conditions. For software testing, probabilistic behavior means that testers should not assume that a generated answer is always reproducible or consistently correct. Important generated artifacts should therefore be reviewed and validated. Understanding this characteristic is particularly important when using GenAI for test design, test data generation, defect analysis, or other activities where accuracy and consistency are important.<\/span><\/p>\n

                                              Question 3<\/b><\/h3>\n

                                              A tester provides a GenAI model with a role, task, context, constraints, and expected output format. What is this collection primarily intended to improve?<\/b><\/p>\n

                                                \n
                                              1. Source-code compilation<\/span><\/li>\n
                                              2. Prompt effectiveness<\/span><\/li>\n
                                              3. Network performance<\/span><\/li>\n
                                              4. Database normalization<\/span><\/li>\n<\/ol>\n

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

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

                                                Providing a clear role, task, context, constraints, and desired output format can improve prompt effectiveness. A well-structured prompt gives the GenAI model more relevant information about what the tester wants to accomplish. For example, a tester may specify that the model should act as a test analyst, provide requirements as context, request boundary-value test ideas, and require a structured table as output. Better prompts can reduce ambiguity and make generated results more useful. However, even a well-designed prompt does not guarantee correctness. Generated content should still be reviewed, validated, and adjusted by a qualified tester.<\/span><\/p>\n

                                                Question 4<\/b><\/h3>\n

                                                Which statement best describes hallucination in the context of GenAI?<\/b><\/p>\n

                                                  \n
                                                1. The model refusing to generate any output<\/span><\/li>\n
                                                2. The model generating a response that is factually incorrect but presented as if it were valid<\/span><\/li>\n
                                                3. The model producing output in a requested format<\/span><\/li>\n
                                                4. The model using information supplied in the prompt<\/span><\/li>\n<\/ol>\n

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

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

                                                  A hallucination occurs when a GenAI system generates information that appears plausible but is incorrect, unsupported, or fabricated. The response may be expressed confidently even though the underlying information is not reliable. In software testing, hallucinations can create risks if testers accept generated test cases, requirements interpretations, defect explanations, or code without verification. Testers should therefore validate important GenAI outputs against authoritative requirements, source code, documentation, or other reliable evidence. Prompt quality can help reduce some errors, but it cannot completely eliminate hallucinations. Human review remains an important safeguard when GenAI is used in testing activities.<\/span><\/p>\n

                                                  Question 5<\/b><\/h3>\n

                                                  A tester asks a GenAI tool to create test cases from a requirement. What should the tester do before using the generated test cases?<\/b><\/p>\n

                                                    \n
                                                  1. Accept them automatically because the model generated them<\/span><\/li>\n
                                                  2. Delete all manually created test cases<\/span><\/li>\n
                                                  3. Execute them without reviewing their relevance<\/span><\/li>\n
                                                  4. Review and validate them against the requirement and testing objectives<\/span><\/li>\n<\/ol>\n

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

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

                                                    Generated test cases should be reviewed and validated before they are incorporated into the testing process. GenAI may misunderstand requirements, omit important scenarios, create redundant tests, or introduce incorrect assumptions. The tester should compare generated cases with the original requirements, identify missing coverage, verify expected results, and assess whether the tests address appropriate risks. Human expertise remains important because GenAI does not inherently understand the complete business context or organizational priorities. Automatic acceptance can therefore introduce defects into the testing process itself. Validation ensures that generated test cases are relevant, accurate, and aligned with the intended testing objectives.<\/span><\/p>\n

                                                    Question 6<\/b><\/h3>\n

                                                    Which testing activity can GenAI commonly support by generating multiple realistic variations of input information?<\/b><\/p>\n

                                                      \n
                                                    1. Test data generation<\/span><\/li>\n
                                                    2. Production deployment approval<\/span><\/li>\n
                                                    3. Organizational budgeting<\/span><\/li>\n
                                                    4. Hardware installation<\/span><\/li>\n<\/ol>\n

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

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

                                                      GenAI can assist with test data generation by creating variations of input values based on specified requirements and constraints. For example, a tester can request valid, invalid, boundary, unusual, or representative data for a particular field or business scenario. Generated data can help expand test coverage and reduce manual effort. However, testers must ensure that generated data is appropriate, syntactically valid, and compliant with security and privacy requirements. Sensitive production information should not be exposed to a GenAI system without appropriate authorization and controls. GenAI-generated test data should therefore be reviewed before being used in actual testing.<\/span><\/p>\n

                                                      Question 7<\/b><\/h3>\n

                                                      Which factor is most important when deciding whether sensitive testing information should be entered into an external GenAI service?<\/b><\/p>\n

                                                        \n
                                                      1. The length of the prompt<\/span><\/li>\n
                                                      2. The number of test cases already written<\/span><\/li>\n
                                                      3. Data privacy and security requirements<\/span><\/li>\n
                                                      4. The programming language used by the application<\/span><\/li>\n<\/ol>\n

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

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

                                                        Data privacy and security requirements are critical when deciding whether testing information can be submitted to an external GenAI service. Test artifacts may contain confidential requirements, personal information, credentials, proprietary source code, customer information, or security-sensitive details. Organizations should understand how the service handles submitted information, including storage, retention, access, and potential model-training practices. Appropriate organizational policies and contractual requirements should also be considered. Testers should avoid exposing sensitive information unless the use is authorized and appropriate safeguards are in place. Security and privacy considerations therefore take priority over convenience when selecting GenAI services for testing.<\/span><\/p>\n

                                                        Question 8<\/b><\/h3>\n

                                                        A tester asks a GenAI system to generate test cases but provides no requirements, business rules, or application context. What is the most likely consequence?<\/b><\/p>\n

                                                          \n
                                                        1. The model will automatically discover all business rules<\/span><\/li>\n
                                                        2. The generated tests may be generic or poorly aligned with the actual system<\/span><\/li>\n
                                                        3. The model will guarantee complete test coverage<\/span><\/li>\n
                                                        4. The generated tests will always match production behavior<\/span><\/li>\n<\/ol>\n

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

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

                                                          GenAI needs sufficient context to generate useful testing artifacts. Without requirements, business rules, system behavior, or other relevant information, the model may rely on general patterns rather than the specific characteristics of the application. The resulting test cases may therefore be generic, incomplete, irrelevant, or based on assumptions that do not apply to the system. Providing meaningful context can improve the relevance of generated results. Nevertheless, additional context does not guarantee complete coverage or correctness. Testers should evaluate the generated tests against requirements, risks, and expected system behavior before using them as part of a testing strategy.<\/span><\/p>\n

                                                          Question 9<\/b><\/h3>\n

                                                          Which approach can help a tester obtain more consistent and useful results from a GenAI model?<\/b><\/p>\n

                                                            \n
                                                          1. Use increasingly ambiguous instructions<\/span><\/li>\n
                                                          2. Remove all context from the prompt<\/span><\/li>\n
                                                          3. Provide clear instructions, relevant context, constraints, and an expected output structure<\/span><\/li>\n
                                                          4. Ask unrelated questions in the same prompt without clarification<\/span><\/li>\n<\/ol>\n

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

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

                                                            Clear instructions, relevant context, constraints, and an expected output structure can improve the usefulness and consistency of GenAI responses. For example, a tester can specify the application area, testing objective, required test design technique, assumptions, and desired format. This gives the model a clearer understanding of the task. Ambiguous prompts can result in broader or less predictable responses because the model must make more assumptions. Even with carefully designed prompts, however, generated content can contain errors or omissions. Testers should therefore combine effective prompting with human review, validation, and appropriate testing expertise.<\/span><\/p>\n

                                                            Question 10<\/b><\/h3>\n

                                                            What is one potential benefit of using GenAI to generate software test cases?<\/b><\/p>\n

                                                              \n
                                                            1. It can reduce the time required to create initial test ideas<\/span><\/li>\n
                                                            2. It guarantees that every requirement is covered<\/span><\/li>\n
                                                            3. It eliminates the need for test design techniques<\/span><\/li>\n
                                                            4. It proves that the application is defect-free<\/span><\/li>\n<\/ol>\n

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

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

                                                              One potential benefit of GenAI is that it can accelerate the creation of initial test ideas and testing artifacts. A tester can provide requirements or other context and ask the model to suggest scenarios, test conditions, or candidate test cases. This can reduce manual effort during early test design activities. However, generated test cases do not guarantee complete requirements coverage, correct application behavior, or defect detection. Testers still need to review the results, identify gaps, apply appropriate test design techniques, and adapt the generated content to the specific application and risk profile. GenAI therefore supports productivity rather than replacing professional testing judgment.<\/span><\/p>\n

                                                              Question 11<\/b><\/h3>\n

                                                              Why should testers be cautious when using GenAI-generated explanations of software defects?<\/b><\/p>\n

                                                                \n
                                                              1. GenAI cannot generate text<\/span><\/li>\n
                                                              2. GenAI may infer an incorrect root cause from incomplete or misleading information<\/span><\/li>\n
                                                              3. Defects never require investigation<\/span><\/li>\n
                                                              4. Generated explanations are always identical<\/span><\/li>\n<\/ol>\n

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

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

                                                                GenAI may generate a plausible explanation of a defect even when the available evidence is incomplete or misleading. It can infer relationships that are not actually present in the application or incorrectly identify a likely root cause. This is especially important when analyzing complex failures involving multiple components, dependencies, or environmental conditions. Testers should compare generated explanations with logs, source code, reproduction steps, configuration information, and other available evidence. GenAI can help organize information and suggest hypotheses, but those hypotheses should not automatically be treated as confirmed findings. Human investigation remains necessary before assigning a definitive root cause.<\/span><\/p>\n

                                                                Question 12<\/b><\/h3>\n

                                                                Which practice is most appropriate when a GenAI model generates test automation code?<\/b><\/p>\n

                                                                  \n
                                                                1. Execute the code immediately in production<\/span><\/li>\n
                                                                2. Trust the code because it was generated from a technical prompt<\/span><\/li>\n
                                                                3. Review, test, and validate the generated code before use<\/span><\/li>\n
                                                                4. Remove all existing automation scripts<\/span><\/li>\n<\/ol>\n

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

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

                                                                  GenAI-generated test automation code should be reviewed, tested, and validated before being integrated into a testing environment. Generated code may contain syntax errors, incorrect assumptions about frameworks, inappropriate locators, security weaknesses, inefficient logic, or incorrect expected results. The tester should verify that the code follows project standards and accurately implements the intended test. Running unreviewed generated code in production can introduce unnecessary risks. GenAI can accelerate coding tasks, but software generated by an AI system remains subject to the same quality, security, maintainability, and correctness requirements as manually written code.<\/span><\/p>\n

                                                                  Question 13<\/b><\/h3>\n

                                                                  Which concept describes providing examples of desired inputs and outputs in a prompt to guide a GenAI model?<\/b><\/p>\n

                                                                    \n
                                                                  1. Few-shot prompting<\/span><\/li>\n
                                                                  2. Regression testing<\/span><\/li>\n
                                                                  3. Exploratory testing<\/span><\/li>\n
                                                                  4. Mutation testing<\/span><\/li>\n<\/ol>\n

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

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

                                                                    Few-shot prompting involves providing a small number of examples that demonstrate the expected relationship between inputs and outputs. These examples can help guide a GenAI model toward the desired response pattern. For example, a tester may provide several requirements together with corresponding test cases and then ask the model to generate additional cases in the same style. This differs from zero-shot prompting, where no examples are provided. Few-shot prompting can improve output structure and task understanding, but it does not guarantee factual correctness. Generated results should still be reviewed against requirements and testing objectives.<\/span><\/p>\n

                                                                    Question 14<\/b><\/h3>\n

                                                                    A tester asks a GenAI model to identify security vulnerabilities in source code. What is an important limitation to consider?<\/b><\/p>\n

                                                                      \n
                                                                    1. The model may miss vulnerabilities or incorrectly identify non-vulnerable code as vulnerable<\/span><\/li>\n
                                                                    2. The model can mathematically prove that the code is secure<\/span><\/li>\n
                                                                    3. The model automatically performs penetration testing on every system<\/span><\/li>\n
                                                                    4. The model guarantees complete security coverage<\/span><\/li>\n<\/ol>\n

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

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

                                                                      GenAI can assist with identifying potential security issues, but it cannot guarantee complete vulnerability detection or prove that software is secure. The model may overlook vulnerabilities, misunderstand application context, or incorrectly flag safe code as problematic. Security testing should therefore use appropriate specialized tools, techniques, and human expertise in addition to GenAI assistance. Testers should validate suspected vulnerabilities and investigate their actual impact and exploitability. GenAI-generated security findings should be treated as potential observations or hypotheses until verified. This approach reduces the risk of false confidence and helps ensure that important security weaknesses are investigated appropriately.<\/span><\/p>\n

                                                                      Question 15<\/b><\/h3>\n

                                                                      Which factor can influence the quality of output generated by a GenAI model for testing purposes?<\/b><\/p>\n

                                                                        \n
                                                                      1. Only the tester’s job title<\/span><\/li>\n
                                                                      2. The quality and relevance of the information provided to the model<\/span><\/li>\n
                                                                      3. The number of computers in the test lab<\/span><\/li>\n
                                                                      4. The physical size of the application server<\/span><\/li>\n<\/ol>\n

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

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

                                                                        The quality and relevance of information supplied to a GenAI model can significantly influence the usefulness of its output. Relevant requirements, constraints, examples, domain information, expected behavior, and other context can help the model produce more appropriate testing artifacts. Poor, incomplete, outdated, or misleading information can result in similarly problematic outputs. Other factors can also influence results, including the model, prompt structure, generation settings, and task complexity. Testers should therefore provide accurate and relevant context whenever possible. Even high-quality input does not eliminate the need for validation because GenAI outputs can still contain inaccuracies or omissions.<\/span><\/p>\n

                                                                        Question 16<\/b><\/h3>\n

                                                                        What should a tester do if a GenAI-generated test case conflicts with an approved software requirement?<\/b><\/p>\n

                                                                          \n
                                                                        1. Use the generated test case because AI is more current<\/span><\/li>\n
                                                                        2. Ignore the approved requirement<\/span><\/li>\n
                                                                        3. Validate the conflict and use the authoritative requirement as the basis for the test<\/span><\/li>\n
                                                                        4. Delete the entire test suite<\/span><\/li>\n<\/ol>\n

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

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

                                                                          Approved requirements are authoritative sources for determining expected system behavior unless they are formally changed through the appropriate process. If a GenAI-generated test case conflicts with an approved requirement, the tester should investigate the discrepancy rather than automatically accepting the AI output. The generated test may be based on an incorrect assumption or misunderstood context. The tester should validate the requirement, clarify any ambiguity with the appropriate stakeholders, and update the test case if necessary. This illustrates an important principle of using GenAI in testing: generated content is an aid and should be evaluated against reliable project information.<\/span><\/p>\n

                                                                          Question 17<\/b><\/h3>\n

                                                                          Which risk may arise when testers become overly dependent on GenAI-generated testing suggestions?<\/b><\/p>\n

                                                                            \n
                                                                          1. Increased need for human review<\/span><\/li>\n
                                                                          2. Reduced critical thinking and insufficient independent analysis<\/span><\/li>\n
                                                                          3. Improved understanding of requirements<\/span><\/li>\n
                                                                          4. Better traceability automatically<\/span><\/li>\n<\/ol>\n

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

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

                                                                            Over-reliance on GenAI can create a risk that testers reduce their own critical thinking and accept generated suggestions without sufficient evaluation. GenAI may produce plausible but incomplete or incorrect outputs, so testers need to maintain independent judgment. Excessive dependence can also cause teams to overlook domain-specific risks, unusual scenarios, or requirements that the model did not recognize. Human expertise remains important for interpreting business context, assessing risk, designing effective tests, and determining whether generated artifacts are appropriate. GenAI should therefore augment testing expertise rather than replace the tester’s responsibility for analysis and decision-making.<\/span><\/p>\n

                                                                            Question 18<\/b><\/h3>\n

                                                                            Which type of testing artifact can GenAI potentially help summarize from large amounts of testing information?<\/b><\/p>\n

                                                                              \n
                                                                            1. Test execution results and defect information<\/span><\/li>\n
                                                                            2. Physical network cables<\/span><\/li>\n
                                                                            3. Computer power supplies<\/span><\/li>\n
                                                                            4. Office furniture inventories<\/span><\/li>\n<\/ol>\n

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

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

                                                                              GenAI can assist in summarizing large amounts of textual testing information, such as test execution results, defect reports, logs, requirements, or testing notes, when the relevant information is provided appropriately. Summarization can help testers identify important trends, recurring issues, outstanding defects, and key observations more efficiently. However, summaries may omit details or misinterpret information, especially when the input is complex or ambiguous. Testers should therefore verify important conclusions against the original evidence. GenAI is particularly useful for reducing the manual effort involved in processing large quantities of information, but human review remains important for consequential decisions.<\/span><\/p>\n

                                                                              Question 19<\/b><\/h3>\n

                                                                              Which statement best describes the role of a human tester when using GenAI for software testing?<\/b><\/p>\n

                                                                                \n
                                                                              1. The tester should accept all generated content without review<\/span><\/li>\n
                                                                              2. The tester is responsible only for writing prompts<\/span><\/li>\n
                                                                              3. The tester should validate, interpret, and make appropriate decisions about generated outputs<\/span><\/li>\n
                                                                              4. The tester is no longer responsible for testing quality<\/span><\/li>\n<\/ol>\n

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

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

                                                                                Human testers remain responsible for evaluating and appropriately using GenAI-generated outputs. This includes reviewing accuracy, relevance, completeness, risks, and alignment with requirements and testing objectives. Testers also provide domain knowledge and judgment that GenAI may not possess. Writing effective prompts is useful, but the tester’s role extends beyond prompt creation. Automatically accepting generated content can introduce errors into test cases, automation, defect analysis, or test reporting. GenAI should therefore be treated as a supporting tool that can increase productivity and generate ideas while the tester maintains responsibility for validation, interpretation, risk assessment, and testing decisions.<\/span><\/p>\n

                                                                                Question 20<\/b><\/h3>\n

                                                                                What is an important consideration when introducing GenAI into an organization’s software testing process?<\/b><\/p>\n

                                                                                  \n
                                                                                1. Establishing appropriate governance, security, privacy, and usage guidelines<\/span><\/li>\n
                                                                                2. Allowing unrestricted access to all confidential information<\/span><\/li>\n
                                                                                3. Removing all existing testing procedures<\/span><\/li>\n
                                                                                4. Assuming that generated outputs require no verification<\/span><\/li>\n<\/ol>\n

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

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

                                                                                  Introducing GenAI into software testing requires appropriate governance and controls. Organizations should define acceptable use, data handling requirements, privacy protections, security controls, review responsibilities, and expectations for validating generated content. Existing testing procedures should not automatically be removed because GenAI output can contain errors, omissions, or inappropriate assumptions. Access to confidential information should also be controlled according to organizational policies and applicable requirements. Clear guidelines help testers use GenAI responsibly while managing associated risks. Governance should address not only technical capabilities but also human oversight, accountability, data protection, and the quality assurance of generated testing artifacts.<\/span><\/p>\n

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

                                                                                  View Full ISTQB CT-GenAI Exam Dumps and Practice Test Dumps.   Question 1 What is the primary purpose of generative artificial intelligence (GenAI) in software testing? To replace all human testers in the software development lifecycle To eliminate the need for software requirements To support testing activities by generating or transforming relevant testing artifacts and […]<\/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\/25060"}],"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=25060"}],"version-history":[{"count":1,"href":"https:\/\/www.examlabs.com\/certification\/wp-json\/wp\/v2\/posts\/25060\/revisions"}],"predecessor-version":[{"id":25061,"href":"https:\/\/www.examlabs.com\/certification\/wp-json\/wp\/v2\/posts\/25060\/revisions\/25061"}],"wp:attachment":[{"href":"https:\/\/www.examlabs.com\/certification\/wp-json\/wp\/v2\/media?parent=25060"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.examlabs.com\/certification\/wp-json\/wp\/v2\/categories?post=25060"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.examlabs.com\/certification\/wp-json\/wp\/v2\/tags?post=25060"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}