{"id":25080,"date":"2026-09-30T11:50:26","date_gmt":"2026-09-30T11:50:26","guid":{"rendered":"https:\/\/www.examlabs.com\/certification\/?p=25080"},"modified":"2026-09-30T11:50:26","modified_gmt":"2026-09-30T11:50:26","slug":"istqb-ct-genai-practice-test-questions-and-exam-dumps-part11-q201-220","status":"publish","type":"post","link":"https:\/\/www.examlabs.com\/certification\/istqb-ct-genai-practice-test-questions-and-exam-dumps-part11-q201-220\/","title":{"rendered":"ISTQB CT-GenAI Practice Test Questions and Exam Dumps Part11 Q201-220"},"content":{"rendered":"

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

 <\/p>\n

Question 201<\/b><\/h3>\n

A tester asks a GenAI tool to generate test cases from a requirement containing several business rules. Which approach is most appropriate for improving the generated results?<\/b><\/p>\n

    \n
  1. Remove the business rules from the prompt<\/span><\/li>\n
  2. Provide the relevant rules and clearly identify the expected testing scope<\/span><\/li>\n
  3. Ask the model to ignore requirement details<\/span><\/li>\n
  4. Provide only the name of the application<\/span><\/li>\n<\/ol>\n

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

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

    Business rules often define important conditions that determine how software should behave. Providing those rules to GenAI gives the model useful context for generating relevant test scenarios. The tester should also clearly identify the testing scope so that the model focuses on the intended functionality rather than unrelated features. Removing business rules or ignoring requirement details forces the model to rely more heavily on assumptions. Providing only the application name supplies very little useful context. Even with detailed requirements, generated test cases should be reviewed against the approved specification to confirm correctness, completeness, and traceability before they are incorporated into formal testing activities.<\/span><\/p>\n

    Question 202<\/b><\/h3>\n

    Which GenAI output characteristic describes whether generated test cases contain all important conditions required by the testing objective?<\/b><\/p>\n

      \n
    1. Completeness<\/span><\/li>\n
    2. Tokenization<\/span><\/li>\n
    3. Temperature<\/span><\/li>\n
    4. Formatting<\/span><\/li>\n<\/ol>\n

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

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

      Completeness refers to whether the generated output adequately covers the information or scenarios required for the intended objective. In testing, a complete set of generated cases should address relevant requirements, conditions, risks, boundaries, and other applicable scenarios within the defined scope. Completeness does not mean generating the largest possible number of test cases. A large collection can still contain gaps or redundancy. Tokenization concerns how text is represented internally, temperature can influence generation behavior, and formatting concerns presentation. Testers should evaluate completeness against requirements and coverage objectives because GenAI may overlook important scenarios even when it produces a large and convincing set of test cases.<\/span><\/p>\n

      Question 203<\/b><\/h3>\n

      A tester wants GenAI to generate test cases for both valid and invalid input values. Which instruction is most useful?<\/b><\/p>\n

        \n
      1. Request only successful scenarios<\/span><\/li>\n
      2. Specify that both positive and negative conditions are required<\/span><\/li>\n
      3. Ask for a shorter response<\/span><\/li>\n
      4. Remove input constraints from the prompt<\/span><\/li>\n<\/ol>\n

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

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

        Explicitly requesting both positive and negative conditions helps the GenAI model consider normal and abnormal system behavior. Positive tests can verify that valid inputs produce expected results, while negative tests can examine how the system responds to invalid, unexpected, incomplete, or unauthorized inputs. Without such an instruction, generated content may focus heavily on common successful scenarios. Removing input constraints can also encourage unsupported assumptions. The tester should provide the actual validation rules and relevant requirements when possible. Generated cases should then be reviewed to ensure that the selected values and expected results accurately represent the application’s defined behavior.<\/span><\/p>\n

        Question 204<\/b><\/h3>\n

        A tester uses GenAI to summarize test execution results for project stakeholders. What should be done before distributing the generated summary?<\/b><\/p>\n

          \n
        1. Verify the summary against the actual test evidence<\/span><\/li>\n
        2. Remove all failed tests from the summary<\/span><\/li>\n
        3. Accept the summary without review<\/span><\/li>\n
        4. Ask the model to invent missing results<\/span><\/li>\n<\/ol>\n

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

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

          A test summary should accurately represent the actual testing performed and its results. GenAI can help transform execution records into concise language, but generated summaries may contain omissions, incorrect numbers, or unsupported statements. The tester should therefore compare the summary with verified execution evidence before distributing it. Failed tests and relevant risks should not be removed simply to make the report appear more positive. Missing information should be investigated rather than invented. Human review is particularly important when summaries are used for release decisions or stakeholder communication. GenAI is useful for improving documentation efficiency, but factual accuracy must remain based on reliable testing evidence.<\/span><\/p>\n

          Question 205<\/b><\/h3>\n

          Which situation is an example of using GenAI for test maintenance?<\/b><\/p>\n

            \n
          1. Generating a new company logo<\/span><\/li>\n
          2. Updating test scripts after a user-interface change<\/span><\/li>\n
          3. Designing a completely unrelated product<\/span><\/li>\n
          4. Removing all regression tests<\/span><\/li>\n<\/ol>\n

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

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

            Test maintenance involves keeping testing artifacts effective as the software, requirements, interfaces, or environments change. GenAI can assist by analyzing updated application elements and suggesting modifications to affected automation scripts, locators, assertions, or test data. For example, after a user-interface change, an AI assistant may help identify selectors that need updating and generate revised automation code. The generated changes should still be reviewed and executed because AI output may contain incorrect assumptions. Generating unrelated content does not support test maintenance, and removing regression tests would reduce coverage. GenAI should therefore be viewed as an aid for maintaining test artifacts rather than an automatic replacement for tester review.<\/span><\/p>\n

            Question 206<\/b><\/h3>\n

            A tester supplies confidential source code to a GenAI service without checking the organization’s policy. Which risk is most relevant?<\/b><\/p>\n

              \n
            1. Increased test coverage<\/span><\/li>\n
            2. Unauthorized disclosure of proprietary information<\/span><\/li>\n
            3. Improved defect traceability<\/span><\/li>\n
            4. Reduced execution time<\/span><\/li>\n<\/ol>\n

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

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

              Confidential source code may contain proprietary algorithms, business logic, security controls, credentials, or other sensitive information. Sending it to a GenAI service without checking whether that service is approved can create risks involving unauthorized access, retention, processing, or disclosure. Testers should follow organizational policies governing AI services and confidential information. Where appropriate, sensitive portions should be minimized, masked, anonymized, or replaced with representative examples. The potential productivity benefit does not remove the need for data protection. Test teams should understand how the selected AI service handles submitted information and ensure that its use is consistent with organizational security, privacy, and intellectual-property requirements.<\/span><\/p>\n

              Question 207<\/b><\/h3>\n

              A tester asks GenAI to generate tests using a particular format and provides one example of the desired output. Which combination of techniques is being used?<\/b><\/p>\n

                \n
              1. Few-shot prompting and output constraints<\/span><\/li>\n
              2. Data poisoning and regression testing<\/span><\/li>\n
              3. Model retraining and tokenization<\/span><\/li>\n
              4. Random testing and compilation<\/span><\/li>\n<\/ol>\n

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

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

                Providing an example of the desired output is a form of few-shot prompting, even when only one example is supplied. Specifying a required format acts as an output constraint because it tells the model how the response should be structured. Together, these techniques can improve consistency and make generated testing artifacts easier to review. The example should accurately demonstrate the intended structure because the model may follow patterns shown in the example. Neither technique guarantees correctness, completeness, or traceability. The tester should still validate the generated cases against requirements and testing objectives. Data poisoning, retraining, tokenization, and compilation describe different technical concepts.<\/span><\/p>\n

                Question 208<\/b><\/h3>\n

                A GenAI model produces a test case that requires a feature not present in the application. What should the tester do?<\/b><\/p>\n

                  \n
                1. Add the feature to the application automatically<\/span><\/li>\n
                2. Treat the test as valid because the model suggested it<\/span><\/li>\n
                3. Identify the unsupported assumption and remove or revise the test<\/span><\/li>\n
                4. Ignore the application specification<\/span><\/li>\n<\/ol>\n

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

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

                  GenAI can generate plausible scenarios based on assumptions that are not supported by the actual application. If a generated test requires a feature that does not exist, the tester should identify the unsupported assumption and determine whether the test should be removed or revised. If the feature is actually planned but missing from the current version, that situation should be addressed through the appropriate requirements and development processes rather than by silently treating the generated test as valid. Comparing AI-generated tests with current specifications and application behavior helps prevent irrelevant tests from entering the test suite and maintains alignment with the actual system.<\/span><\/p>\n

                  Question 209<\/b><\/h3>\n

                  Which activity best demonstrates human-in-the-loop use of GenAI in testing?<\/b><\/p>\n

                    \n
                  1. The model independently approves all generated tests<\/span><\/li>\n
                  2. The tester reviews AI-generated tests and decides which are appropriate<\/span><\/li>\n
                  3. The model executes production changes without approval<\/span><\/li>\n
                  4. The tester accepts every AI recommendation automatically<\/span><\/li>\n<\/ol>\n

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

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

                    Human-in-the-loop means that a person remains actively involved in reviewing, validating, or making decisions about AI-generated results. In testing, this can involve asking GenAI to generate test cases, reviewing those cases against requirements and risks, correcting unsuitable content, and deciding which cases should be adopted. The model assists with the work but does not independently determine the final outcome. Automatic approval or execution without appropriate oversight removes the human control that the approach is intended to provide. Human-in-the-loop practices are particularly valuable for high-impact testing decisions where incorrect AI-generated content could affect quality, security, compliance, or release decisions.<\/span><\/p>\n

                    Question 210<\/b><\/h3>\n

                    A tester notices that GenAI-generated test cases use terminology inconsistent with the project’s approved glossary. What is the best action?<\/b><\/p>\n

                      \n
                    1. Accept the terminology because synonyms are always equivalent<\/span><\/li>\n
                    2. Remove all domain-specific terms<\/span><\/li>\n
                    3. Align the generated content with the approved terminology<\/span><\/li>\n
                    4. Ignore the issue because terminology does not matter<\/span><\/li>\n<\/ol>\n

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

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

                      Consistent terminology is important because project-specific terms may have defined meanings. Using the approved glossary helps ensure that test cases are understandable and accurately aligned with requirements, business processes, and other project documentation. GenAI may generate synonyms that appear acceptable in general language but could introduce ambiguity or alter the intended meaning in a technical context. The tester should therefore correct generated content or provide the approved terminology as context for future prompts. Removing domain-specific terms would reduce clarity, while ignoring terminology differences can create misunderstandings. Standardized language also supports traceability and communication between testers and other project stakeholders.<\/span><\/p>\n

                      Question 211<\/b><\/h3>\n

                      A tester asks GenAI to identify possible security weaknesses from a set of application requirements. What should the tester consider about the generated findings?<\/b><\/p>\n

                        \n
                      1. They are hypotheses requiring security validation<\/span><\/li>\n
                      2. They are automatically confirmed vulnerabilities<\/span><\/li>\n
                      3. They should be reported without evidence<\/span><\/li>\n
                      4. They replace security testing completely<\/span><\/li>\n<\/ol>\n

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

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

                        GenAI can assist security testing by suggesting possible weaknesses, attack scenarios, or areas requiring investigation. However, generated findings are not automatically confirmed vulnerabilities. The tester should validate each suggestion using appropriate security testing techniques, evidence, application behavior, and relevant security requirements. Some generated suggestions may be irrelevant, technically incorrect, or based on assumptions. Reporting unsupported claims can lead to inaccurate defect information and unnecessary investigation. GenAI can therefore be valuable for brainstorming and expanding security test ideas, but it should complement rather than replace systematic security testing and expert review. Evidence remains necessary before classifying a generated suggestion as a confirmed security issue.<\/span><\/p>\n

                        Question 212<\/b><\/h3>\n

                        Which prompt element is most useful when asking GenAI to generate tests for a specific user role?<\/b><\/p>\n

                          \n
                        1. The expected response length only<\/span><\/li>\n
                        2. The relevant role, permissions, and associated requirements<\/span><\/li>\n
                        3. The model’s preferred programming language<\/span><\/li>\n
                        4. Unrelated historical project notes<\/span><\/li>\n<\/ol>\n

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

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

                          User roles and permissions can significantly affect the expected behavior of an application. Providing the relevant role, permissions, restrictions, and associated requirements gives GenAI the context needed to generate meaningful role-specific tests. For example, an administrator may have permissions that a regular user does not have, creating different positive and negative test conditions. Without this information, the model may assume inappropriate access levels. Response length or programming language does not provide the necessary authorization context, while unrelated project notes may introduce confusion. Generated role-based tests should still be checked against the actual authorization model and approved security requirements before being used.<\/span><\/p>\n

                          Question 213<\/b><\/h3>\n

                          A tester wants to reduce irrelevant output when asking GenAI to create regression tests. Which action is most effective?<\/b><\/p>\n

                            \n
                          1. Add more unrelated context<\/span><\/li>\n
                          2. Clearly define the changed functionality and regression scope<\/span><\/li>\n
                          3. Remove all acceptance criteria<\/span><\/li>\n
                          4. Ask the model to generate every possible test<\/span><\/li>\n<\/ol>\n

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

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

                            Clearly defining the changed functionality and regression scope helps GenAI focus on areas that may have been affected by the change. Useful context can include modified requirements, affected components, interfaces, dependencies, and existing related tests. This reduces the likelihood that the model will generate large amounts of unrelated testing content. Removing acceptance criteria can make the model more dependent on assumptions, while requesting every possible test can increase redundancy and irrelevant output. Regression testing should remain focused on verifying that changes have not introduced unintended effects. GenAI can help identify candidate regression tests, but the tester should determine their final suitability based on impact and risk.<\/span><\/p>\n

                            Question 214<\/b><\/h3>\n

                            What is a potential consequence of relying on GenAI-generated test data without checking whether it satisfies application validation rules?<\/b><\/p>\n

                              \n
                            1. The data may cause misleading or invalid test results<\/span><\/li>\n
                            2. The application will automatically become more secure<\/span><\/li>\n
                            3. All production scenarios will be represented<\/span><\/li>\n
                            4. Data validation becomes unnecessary<\/span><\/li>\n<\/ol>\n

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

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

                              Generated test data can look realistic while still violating the actual application’s data constraints. For example, a model may create invalid identifiers, unsupported formats, impossible dates, or values outside the allowed range. If testers do not validate the generated data, tests may fail for reasons unrelated to the intended scenario or may pass without exercising meaningful conditions. Therefore, generated data should be checked against application validation rules and the testing objective. Synthetic data can reduce privacy risks, but it does not automatically guarantee technical validity. Testers should confirm that generated values are appropriate for positive, negative, boundary, and other required testing conditions.<\/span><\/p>\n

                              Question 215<\/b><\/h3>\n

                              Which characteristic is particularly important when GenAI generates a test case for a safety-critical or high-risk system?<\/b><\/p>\n

                                \n
                              1. The response should be entertaining<\/span><\/li>\n
                              2. The response should contain maximum wording<\/span><\/li>\n
                              3. The generated information should undergo rigorous validation and review<\/span><\/li>\n
                              4. The model should make the final testing decision independently<\/span><\/li>\n<\/ol>\n

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

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

                                High-risk systems require stronger controls because incorrect testing information can have serious consequences. GenAI-generated test cases should therefore undergo rigorous validation against approved requirements, safety constraints, standards, risk assessments, and other authoritative sources. The level of review should reflect the potential impact of errors. A longer or more entertaining response does not make a test case safer or more accurate. Allowing a model to make final testing decisions independently would also reduce appropriate accountability. GenAI can still assist with brainstorming and drafting, but its output should be subject to appropriate expert review and established quality processes before it influences testing activities in high-risk environments.<\/span><\/p>\n

                                Question 216<\/b><\/h3>\n

                                A tester uses GenAI to refactor an existing automation script. What should be preserved during the refactoring?<\/b><\/p>\n

                                  \n
                                1. The intended behavior and testing objectives<\/span><\/li>\n
                                2. Only the comments in the source code<\/span><\/li>\n
                                3. The original number of lines<\/span><\/li>\n
                                4. Every original variable name regardless of usefulness<\/span><\/li>\n<\/ol>\n

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

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

                                  Refactoring aims to improve code structure, readability, maintainability, or efficiency without unintentionally changing its intended behavior. When GenAI assists with refactoring automation code, the tester should ensure that the original testing objectives, assertions, important conditions, and expected behavior remain intact. The number of lines may change significantly, and variable names may be improved when appropriate. Comments can also be updated if they become inaccurate. Generated refactoring should be reviewed and executed to verify that behavior has not changed unexpectedly. The key principle is preserving the intended function of the test while improving its implementation, rather than preserving superficial characteristics of the original code.<\/span><\/p>\n

                                  Question 217<\/b><\/h3>\n

                                  Which practice helps a testing team evaluate whether a GenAI tool is providing useful output over time?<\/b><\/p>\n

                                    \n
                                  1. Establish defined quality and effectiveness criteria<\/span><\/li>\n
                                  2. Accept every generated result as successful<\/span><\/li>\n
                                  3. Measure only how long the prompt is<\/span><\/li>\n
                                  4. Ignore tester feedback<\/span><\/li>\n<\/ol>\n

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

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

                                    Defined quality and effectiveness criteria allow a testing team to assess whether GenAI is actually improving the testing process. Possible criteria can include correctness, relevance, completeness, coverage, time saved, review effort, defect detection, maintainability, and consistency, depending on the use case. Without defined criteria, teams may judge AI output mainly by how impressive or fluent it appears. Tester feedback and actual testing outcomes can provide useful evidence for evaluating the tool. Measuring only prompt length does not demonstrate effectiveness. Regular evaluation can also identify situations where GenAI provides little benefit or where additional controls and review are needed.<\/span><\/p>\n

                                    Question 218<\/b><\/h3>\n

                                    A tester asks GenAI to generate test cases based on a document containing contradictory requirements. What should the tester do?<\/b><\/p>\n

                                      \n
                                    1. Assume the model will always select the correct requirement<\/span><\/li>\n
                                    2. Identify and resolve the contradiction before relying on generated tests<\/span><\/li>\n
                                    3. Use whichever requirement produces more test cases<\/span><\/li>\n
                                    4. Ignore the contradiction<\/span><\/li>\n<\/ol>\n

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

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

                                      Contradictory requirements create uncertainty about the expected system behavior. If GenAI receives conflicting information, it may select one interpretation, combine them incorrectly, or produce inconsistent test cases. The tester should identify the contradiction and seek clarification from the appropriate source or stakeholders before relying on generated tests. The number of generated cases is not a valid basis for deciding which requirement is correct. Ignoring the contradiction can cause incorrect expected results and misleading test coverage. GenAI can help highlight inconsistencies, but resolving business or technical conflicts requires appropriate human and organizational decision-making.<\/span><\/p>\n

                                      Question 219<\/b><\/h3>\n

                                      Which GenAI application can help a tester improve the readability of a complex defect report?<\/b><\/p>\n

                                        \n
                                      1. Rewriting the report into clear language while preserving verified facts<\/span><\/li>\n
                                      2. Removing all technical evidence<\/span><\/li>\n
                                      3. Adding unsupported assumptions<\/span><\/li>\n
                                      4. Changing the defect severity automatically<\/span><\/li>\n<\/ol>\n

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

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

                                        GenAI can assist with improving the clarity and readability of defect reports by restructuring information, simplifying wording, and adapting the presentation for different audiences. The important condition is that verified facts, reproduction information, expected behavior, actual behavior, and relevant evidence should remain accurate. Removing technical evidence can make the report less useful, while adding unsupported assumptions can introduce misleading information. Severity should be determined according to established project criteria rather than changed merely because the wording has been improved. Human review remains necessary after rewriting to ensure that the transformed report preserves the meaning and technical accuracy of the original defect information.<\/span><\/p>\n

                                        Question 220<\/b><\/h3>\n

                                        Which statement best describes an appropriate role for GenAI in software testing?<\/b><\/p>\n

                                          \n
                                        1. An autonomous replacement for testers<\/span><\/li>\n
                                        2. A tool that can assist testing activities while outputs remain subject to appropriate validation<\/span><\/li>\n
                                        3. A guaranteed source of correct requirements<\/span><\/li>\n
                                        4. A system that eliminates the need for test evidence<\/span><\/li>\n<\/ol>\n

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

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

                                          GenAI can support many software testing activities, including test design, test-data generation, automation assistance, documentation, defect analysis, and exploratory testing. Its output can improve productivity and provide additional ideas, but generated content may contain errors, omissions, assumptions, or hallucinations. Therefore, appropriate validation is required before important outputs are used. GenAI should not be treated as an autonomous replacement for testers or as an authoritative source of requirements unless its role has been specifically governed for that purpose. Test evidence and established testing practices remain important. The most practical role is as an assistant that augments tester capabilities while maintaining human oversight and accountability.<\/span><\/p>\n

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

                                          View Full ISTQB CT-GenAI Exam Dumps and Practice Test Dumps.   Question 201 A tester asks a GenAI tool to generate test cases from a requirement containing several business rules. Which approach is most appropriate for improving the generated results? Remove the business rules from the prompt Provide the relevant rules and clearly identify the […]<\/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\/25080"}],"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=25080"}],"version-history":[{"count":1,"href":"https:\/\/www.examlabs.com\/certification\/wp-json\/wp\/v2\/posts\/25080\/revisions"}],"predecessor-version":[{"id":25081,"href":"https:\/\/www.examlabs.com\/certification\/wp-json\/wp\/v2\/posts\/25080\/revisions\/25081"}],"wp:attachment":[{"href":"https:\/\/www.examlabs.com\/certification\/wp-json\/wp\/v2\/media?parent=25080"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.examlabs.com\/certification\/wp-json\/wp\/v2\/categories?post=25080"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.examlabs.com\/certification\/wp-json\/wp\/v2\/tags?post=25080"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}