{"id":25084,"date":"2026-09-30T11:55:28","date_gmt":"2026-09-30T11:55:28","guid":{"rendered":"https:\/\/www.examlabs.com\/certification\/?p=25084"},"modified":"2026-09-30T11:55:28","modified_gmt":"2026-09-30T11:55:28","slug":"istqb-ct-genai-practice-test-questions-and-exam-dumps-part13-q241-260","status":"publish","type":"post","link":"https:\/\/www.examlabs.com\/certification\/istqb-ct-genai-practice-test-questions-and-exam-dumps-part13-q241-260\/","title":{"rendered":"ISTQB CT-GenAI Practice Test Questions and Exam Dumps Part13 Q241-260"},"content":{"rendered":"

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

 <\/p>\n

Question 241<\/b><\/h3>\n

A tester asks a GenAI tool to generate test cases from acceptance criteria. Which action provides the strongest assurance that the generated cases are appropriate?<\/b><\/p>\n

    \n
  1. Review each case against the acceptance criteria and expected behavior<\/span><\/li>\n
  2. Accept every case because the model was given the requirements<\/span><\/li>\n
  3. Select only the longest generated cases<\/span><\/li>\n
  4. Execute the cases without checking their expected results<\/span><\/li>\n<\/ol>\n

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

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

    Generated test cases should be validated against the authoritative acceptance criteria before being accepted. The tester should confirm that each case addresses relevant behavior, contains appropriate inputs, and has an expected result that is consistent with the requirement. Providing requirements to GenAI improves context but does not guarantee that every generated case is correct or complete. Length is not an indicator of quality, and executing unchecked tests can introduce incorrect assumptions into the test process. Human review therefore remains essential. The tester can use GenAI to accelerate test design while retaining responsibility for verifying that generated artifacts accurately represent the intended acceptance criteria.<\/span><\/p>\n

    Question 242<\/b><\/h3>\n

    A GenAI assistant is generating test scenarios for an application with role-based access control. Which information is most important to include in the prompt?<\/b><\/p>\n

      \n
    1. The preferred programming language only<\/span><\/li>\n
    2. The number of test cases required<\/span><\/li>\n
    3. User roles and the permissions associated with each role<\/span><\/li>\n
    4. The desired length of the explanation<\/span><\/li>\n<\/ol>\n

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

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

      Role-based access control testing depends heavily on understanding which actions each role is permitted or prohibited from performing. Providing role definitions and associated permissions enables GenAI to generate more meaningful authorization scenarios, including both allowed and denied operations. Programming language and response length do not provide the essential security context needed for this task. The tester should also include relevant resources, actions, authentication requirements, and expected authorization behavior where available. Generated cases must then be checked against the approved access-control specification. This helps identify missing authorization combinations and prevents unsupported assumptions about user privileges from entering the test suite.<\/span><\/p>\n

      Question 243<\/b><\/h3>\n

      A tester asks GenAI to generate tests for a new feature, but the requirements contain conflicting statements about the expected behavior. What should the tester do?<\/b><\/p>\n

        \n
      1. Ask GenAI to choose whichever requirement appears more reasonable<\/span><\/li>\n
      2. Resolve the conflict with the appropriate requirements or product stakeholders<\/span><\/li>\n
      3. Combine both behaviors into one expected result<\/span><\/li>\n
      4. Ignore the conflicting statements<\/span><\/li>\n<\/ol>\n

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

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

        Conflicting requirements must be resolved by an authorized source rather than allowing GenAI to decide which interpretation is correct. The tester should identify the conflict and obtain clarification from the appropriate product owner, business analyst, requirements owner, or other responsible stakeholder. GenAI can help identify contradictions and formulate clarification questions, but it should not establish the authoritative business behavior. Combining conflicting expectations can produce invalid test cases, while ignoring the issue may result in incomplete or incorrect coverage. Once the conflict has been resolved, the approved interpretation can be incorporated into the test design and used as reliable context for GenAI-assisted generation.<\/span><\/p>\n

        Question 244<\/b><\/h3>\n

        Which situation represents a risk of over-reliance on GenAI in software testing?<\/b><\/p>\n

          \n
        1. A tester uses GenAI to brainstorm additional scenarios<\/span><\/li>\n
        2. A tester uses GenAI to improve the wording of a defect report<\/span><\/li>\n
        3. A tester uses GenAI to suggest possible test data<\/span><\/li>\n
        4. A tester accepts generated tests without reviewing them against requirements<\/span><\/li>\n<\/ol>\n

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

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

          Over-reliance occurs when testers treat GenAI output as authoritative without applying appropriate professional judgment. Generated tests can contain hallucinations, omissions, incorrect assumptions, or mismatches with current requirements. Accepting them without review can therefore introduce defects into the testing process itself. Using GenAI for brainstorming, wording improvements, or test-data suggestions can be appropriate when the outputs are reviewed and validated. The tester remains responsible for determining whether generated content is accurate and useful. A controlled workflow should define where GenAI can assist, what validation is required, and who has authority to approve generated testing artifacts.<\/span><\/p>\n

          Question 245<\/b><\/h3>\n

          A tester wants to generate multiple realistic but non-sensitive customer profiles for functional testing. Which practice is most appropriate?<\/b><\/p>\n

            \n
          1. Use validated synthetic data that meets the required business characteristics<\/span><\/li>\n
          2. Copy a random selection of production customer records<\/span><\/li>\n
          3. Remove only customer names from production records<\/span><\/li>\n
          4. Ask GenAI to reproduce actual customer accounts<\/span><\/li>\n<\/ol>\n

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

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

            Validated synthetic data can provide realistic test conditions while reducing direct exposure to sensitive customer information. The generated records should reflect the characteristics needed for the testing objectives, such as different customer types, valid and invalid values, boundary conditions, and relevant business combinations. Simply removing names may not sufficiently protect privacy because other fields can still identify individuals. Reproducing actual accounts is also inappropriate when sensitive information is involved. Synthetic data should be reviewed for realism, completeness, privacy, and suitability before use. Organizational data-handling policies should continue to apply even when GenAI is used to generate the records.<\/span><\/p>\n

            Question 246<\/b><\/h3>\n

            A tester uses few-shot prompting to improve the format of generated test cases. What does the tester provide in this approach?<\/b><\/p>\n

              \n
            1. A request without any examples<\/span><\/li>\n
            2. Several examples demonstrating the desired input and output pattern<\/span><\/li>\n
            3. Only the model’s technical documentation<\/span><\/li>\n
            4. A random collection of unrelated test cases<\/span><\/li>\n<\/ol>\n

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

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

              Few-shot prompting provides multiple examples that demonstrate how the desired task should be performed or formatted. For test generation, examples can show the expected structure of test cases, terminology, level of detail, and relationship between requirements and tests. The examples should be relevant and representative because poor examples can influence the model toward undesirable output. Few-shot prompting differs from zero-shot prompting, where the task is requested without examples. Even when examples improve consistency, the generated results still require validation. The tester should ensure that examples do not contain confidential information or incorrect assumptions that could be reproduced by the model.<\/span><\/p>\n

              Question 247<\/b><\/h3>\n

              A tester asks GenAI to recommend tests for a third-party API. The model provides information about an API version that is no longer supported. What is the main concern?<\/b><\/p>\n

                \n
              1. The response is too short<\/span><\/li>\n
              2. The generated tests may be based on outdated information<\/span><\/li>\n
              3. The model used technical terminology<\/span><\/li>\n
              4. The tester provided too many requirements<\/span><\/li>\n<\/ol>\n

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

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

                GenAI may produce information that is outdated, especially when its knowledge does not reflect the latest API documentation or service changes. Using obsolete API behavior could result in invalid test cases, incorrect endpoints, unsupported parameters, or misleading expected results. The tester should verify recommendations against current authoritative documentation, release notes, or the provider’s approved specifications. This is particularly important for third-party services that change frequently. GenAI can accelerate test design and help identify possible scenarios, but current external documentation should remain the source of truth for version-specific behavior and supported functionality.<\/span><\/p>\n

                Question 248<\/b><\/h3>\n

                A tester needs GenAI to create tests specifically for performance requirements stating that 95% of requests must complete within two seconds. What should the prompt emphasize?<\/b><\/p>\n

                  \n
                1. Only the application’s user interface<\/span><\/li>\n
                2. The number of developers on the project<\/span><\/li>\n
                3. The response-time threshold and the stated measurement condition<\/span><\/li>\n
                4. The preferred wording style for the test report<\/span><\/li>\n<\/ol>\n

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

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

                  Performance test design depends on measurable requirements and their associated conditions. The statement that 95% of requests must complete within two seconds provides both a threshold and a percentage-based success criterion. The prompt should preserve these details and, where specified, include workload, environment, transaction type, and measurement method. Focusing only on interface details or report wording would not provide the information needed for meaningful performance scenarios. The generated tests should be reviewed against the approved performance requirement to ensure that the threshold, population, and expected result are represented correctly. This helps prevent GenAI from simplifying or changing an important quantitative requirement.<\/span><\/p>\n

                  Question 249<\/b><\/h3>\n

                  Which practice helps maintain traceability when GenAI generates test cases from requirements?<\/b><\/p>\n

                    \n
                  1. Remove requirement references from generated tests<\/span><\/li>\n
                  2. Map each accepted test case to the relevant approved requirement or objective<\/span><\/li>\n
                  3. Allow GenAI to create fictional requirement identifiers<\/span><\/li>\n
                  4. Store only the final test-case descriptions<\/span><\/li>\n<\/ol>\n

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

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

                    Traceability is strengthened when accepted test cases are explicitly associated with approved requirements or test objectives. This relationship helps testers determine which requirements are covered, identify potential gaps, and support impact analysis when requirements change. Generated requirement identifiers should never be accepted simply because the model created them. The tester should use authoritative identifiers from the requirements repository and verify the mapping. Removing references or storing only descriptions makes coverage analysis more difficult. Maintaining traceability also supports reporting, reviews, audits, and regression planning by showing how testing artifacts relate to the intended system behavior.<\/span><\/p>\n

                    Question 250<\/b><\/h3>\n

                    A tester asks GenAI to generate tests for a security-sensitive administrative function. Which approach is most appropriate?<\/b><\/p>\n

                      \n
                    1. Allow the model to invent security requirements<\/span><\/li>\n
                    2. Use the generated tests without review because security testing is automated<\/span><\/li>\n
                    3. Provide relevant approved security requirements and validate the generated tests carefully<\/span><\/li>\n
                    4. Exclude negative security scenarios to avoid false positives<\/span><\/li>\n<\/ol>\n

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

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

                      Security-sensitive functions require accurate context and careful validation. Providing approved security requirements helps GenAI understand the intended authorization, authentication, confidentiality, or other security conditions. The generated tests should then be reviewed by qualified testers because incorrect security assumptions can create significant gaps. GenAI should not invent security requirements, and automation does not eliminate the need for review. Negative security scenarios are often important for testing unauthorized access and improper behavior. A controlled approach combines authoritative requirements, targeted prompting, generated test ideas, human validation, and appropriate security-testing practices to reduce the risk of incomplete or misleading generated content.<\/span><\/p>\n

                      Question 251<\/b><\/h3>\n

                      A team wants to compare two different GenAI prompts for generating regression tests. Which evaluation approach is most useful?<\/b><\/p>\n

                        \n
                      1. Compare the prompts only by response length<\/span><\/li>\n
                      2. Determine which prompt produces the most words<\/span><\/li>\n
                      3. Compare their outputs against predefined quality and coverage criteria<\/span><\/li>\n
                      4. Select the prompt that sounds more confident<\/span><\/li>\n<\/ol>\n

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

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

                        Prompt effectiveness should be evaluated against criteria connected to the testing objective. For regression-test generation, useful criteria may include requirement coverage, defect-relevant scenarios, duplicate rate, correctness, relevance, maintainability, and missing important cases. Response length does not necessarily indicate quality, and confident wording can still contain incorrect information. Comparing outputs systematically helps the team determine which prompting approach better supports its intended task. The evaluation should use representative requirements and consistent conditions where possible. Human review remains necessary because automated measures may not detect every important omission or incorrect assumption in generated test artifacts.<\/span><\/p>\n

                        Question 252<\/b><\/h3>\n

                        A tester provides a GenAI tool with a long document containing hundreds of unrelated project details and asks for test cases for one small feature. What problem may occur?<\/b><\/p>\n

                          \n
                        1. The additional unrelated context may reduce the relevance of the generated output<\/span><\/li>\n
                        2. The model will automatically ignore all unrelated information<\/span><\/li>\n
                        3. The feature will always receive complete test coverage<\/span><\/li>\n
                        4. The model will become deterministic<\/span><\/li>\n<\/ol>\n

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

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

                          Excessive or irrelevant context can make it more difficult for a GenAI system to focus on the information that matters to the requested task. The model may give attention to unrelated details, misunderstand priorities, or produce scenarios that do not match the specific feature. Providing focused context can improve relevance and reduce unnecessary output. This does not mean every large prompt will fail, but testers should consider context limits and information quality when designing prompts. A useful approach is to provide the feature requirements, relevant business rules, roles, constraints, and expected behavior while minimizing unrelated material.<\/span><\/p>\n

                          Question 253<\/b><\/h3>\n

                          A tester asks GenAI to create exploratory testing ideas for a mobile application. Which use of the generated output is most appropriate?<\/b><\/p>\n

                            \n
                          1. Treat every suggestion as an approved test case<\/span><\/li>\n
                          2. Use the suggestions as ideas to guide human exploration<\/span><\/li>\n
                          3. Replace all exploratory testing with generated scripts<\/span><\/li>\n
                          4. Ignore application-specific risks<\/span><\/li>\n<\/ol>\n

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

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

                            GenAI can be useful for brainstorming exploratory testing ideas, including unusual workflows, combinations, edge cases, and questions worth investigating. However, exploratory testing relies on human observation, learning, and adaptation during testing. Generated suggestions should therefore be treated as input to the tester’s investigation rather than automatically approved test cases. The tester can select relevant ideas based on application risks, requirements, user behavior, and observed results. This approach uses GenAI to expand the tester’s thinking while preserving human judgment. It also avoids assuming that the model has complete knowledge of the application or its actual runtime behavior.<\/span><\/p>\n

                            Question 254<\/b><\/h3>\n

                            A tester asks GenAI to rewrite a requirement into clearer language. Which condition should be preserved during the transformation?<\/b><\/p>\n

                              \n
                            1. The original business meaning and acceptance conditions<\/span><\/li>\n
                            2. The model’s preferred terminology<\/span><\/li>\n
                            3. Additional functionality suggested by the model<\/span><\/li>\n
                            4. Any assumptions needed to make the requirement longer<\/span><\/li>\n<\/ol>\n

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

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

                              When GenAI transforms an existing requirement, the original business intent must remain unchanged unless an authorized stakeholder explicitly approves a modification. A clearer wording should preserve functional behavior, constraints, acceptance conditions, and important terminology. Adding new functionality or assumptions can unintentionally change the specification. The tester should compare the rewritten requirement with the source and verify that no important meaning has been lost or introduced. GenAI can improve readability and identify ambiguous wording, but the authoritative requirement remains the basis for comparison. Any actual change in behavior should go through the organization’s established requirements-management process.<\/span><\/p>\n

                              Question 255<\/b><\/h3>\n

                              Which information should generally NOT be entered into an external GenAI service unless explicitly authorized?<\/b><\/p>\n

                                \n
                              1. Publicly available product documentation<\/span><\/li>\n
                              2. Generic testing terminology<\/span><\/li>\n
                              3. Confidential customer information<\/span><\/li>\n
                              4. Publicly documented programming concepts<\/span><\/li>\n<\/ol>\n

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

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

                                Confidential customer information can contain personally identifiable, financial, business-sensitive, or otherwise protected data. Sending such information to an external GenAI service without authorization may create privacy, security, contractual, or compliance risks. Public documentation and generic testing terminology generally present a different risk profile, although organizational policies still determine what may be submitted. Testers should understand data-classification rules and approved GenAI services before sharing project information. Where testing requires sensitive information, approved alternatives may include anonymization, redaction, synthetic data, or authorized internal models. Protecting confidential information is an important part of responsible GenAI adoption.<\/span><\/p>\n

                                Question 256<\/b><\/h3>\n

                                A tester wants generated test cases to follow a consistent structure containing ID, preconditions, steps, expected result, and priority. Which prompting technique is most useful?<\/b><\/p>\n

                                  \n
                                1. Provide a clear output schema or format constraint<\/span><\/li>\n
                                2. Ask the model to be creative<\/span><\/li>\n
                                3. Avoid specifying the desired structure<\/span><\/li>\n
                                4. Request unrelated examples<\/span><\/li>\n<\/ol>\n

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

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

                                  Explicit output constraints help GenAI produce responses in a predictable and usable structure. Specifying fields such as test ID, preconditions, steps, expected result, and priority gives the model clear instructions about the required format. This can make generated cases easier to review, compare, and potentially transfer into test-management systems. Creativity without structure may increase variation rather than consistency. Unrelated examples can also introduce inappropriate patterns. Even when the requested structure is followed correctly, the tester must still verify the content of each field. Formatting consistency improves usability but does not guarantee that the generated test itself is accurate or complete.<\/span><\/p>\n

                                  Question 257<\/b><\/h3>\n

                                  A GenAI-generated test uses a database table that is not mentioned anywhere in the approved system documentation. What should the tester do?<\/b><\/p>\n

                                    \n
                                  1. Assume the table exists because the model generated it<\/span><\/li>\n
                                  2. Verify the database object against authoritative technical documentation<\/span><\/li>\n
                                  3. Add the table to the test environment immediately<\/span><\/li>\n
                                  4. Treat the generated table as a new requirement<\/span><\/li>\n<\/ol>\n

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

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

                                    The generated database object may be a hallucination or may simply reflect an unsupported assumption. The tester should verify it against authoritative architecture, database, or technical documentation before using it in testing. Adding an undocumented object or treating it as a requirement could create confusion and potentially affect the test environment incorrectly. GenAI-generated technical details should be validated just like other generated factual claims. If the object does exist but is absent from available documentation, the appropriate technical owner can clarify its purpose and status. Verification protects the test process from being built around assumptions that have no reliable evidence.<\/span><\/p>\n

                                    Question 258<\/b><\/h3>\n

                                    Which action is most appropriate when a GenAI-generated test summary contains a defect count that differs from the test-management system?<\/b><\/p>\n

                                      \n
                                    1. Publish the GenAI summary because it is easier to read<\/span><\/li>\n
                                    2. Average the two numbers<\/span><\/li>\n
                                    3. Use the authoritative execution data and correct the summary<\/span><\/li>\n
                                    4. Delete the defect information entirely<\/span><\/li>\n<\/ol>\n

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

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

                                      The test-management system or other designated authoritative source should determine the actual defect count. A GenAI-generated summary is a communication aid and should not override verified execution data. The tester should identify the discrepancy, correct the summary, and investigate why the incorrect value was generated if necessary. Averaging two conflicting figures has no factual basis, while deleting the information may make the report incomplete. This example illustrates why generated summaries require factual verification before publication. GenAI can help organize complex results, but the underlying metrics must come from reliable execution records and approved sources.<\/span><\/p>\n

                                      Question 259<\/b><\/h3>\n

                                      A tester uses GenAI to refactor an existing automated test suite. What should be checked in addition to whether the new code runs successfully?<\/b><\/p>\n

                                        \n
                                      1. Whether the refactoring preserves test intent, assertions, coverage, and required behavior<\/span><\/li>\n
                                      2. Only whether the code contains fewer lines<\/span><\/li>\n
                                      3. Only whether the model claims the code is optimized<\/span><\/li>\n
                                      4. Whether all comments have been removed<\/span><\/li>\n<\/ol>\n

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

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

                                        Successful execution alone does not prove that a refactored test preserves its original purpose. The tester should compare the revised code with the original test intent and verify that important setup, actions, assertions, data, coverage, and expected behavior remain intact. The refactoring should also be reviewed for maintainability and unintended side effects. Fewer lines do not necessarily mean better automation, and the model’s own claim about optimization is not sufficient evidence. Removing comments can even reduce maintainability. GenAI-assisted refactoring should therefore be evaluated both technically and from the perspective of whether the resulting tests continue to provide the intended testing value.<\/span><\/p>\n

                                        Question 260<\/b><\/h3>\n

                                        A testing team establishes a policy requiring human approval before any GenAI-generated test artifact enters the controlled test repository. What is the main purpose of this control?<\/b><\/p>\n

                                          \n
                                        1. To prevent testers from using GenAI for brainstorming<\/span><\/li>\n
                                        2. To ensure generated artifacts are reviewed for correctness and compliance before official use<\/span><\/li>\n
                                        3. To guarantee that GenAI never produces incorrect output<\/span><\/li>\n
                                        4. To eliminate all testing activities performed by humans<\/span><\/li>\n<\/ol>\n

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

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

                                          Human approval creates a governance checkpoint between GenAI generation and formal use. Reviewers can check generated artifacts for correctness, requirement alignment, security concerns, confidentiality issues, quality, and compliance with organizational standards. The control does not guarantee that GenAI will never produce errors; instead, it provides an opportunity to identify and correct problems before they become part of the controlled testing repository. Human review also establishes accountability for acceptance decisions. GenAI can remain a productivity tool for drafting and analysis, while the approval process ensures that official testing artifacts meet the organization’s quality and governance expectations.<\/span><\/p>\n

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

                                          View Full ISTQB CT-GenAI Exam Dumps and Practice Test Dumps.   Question 241 A tester asks a GenAI tool to generate test cases from acceptance criteria. Which action provides the strongest assurance that the generated cases are appropriate? Review each case against the acceptance criteria and expected behavior Accept every case because the model was […]<\/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\/25084"}],"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=25084"}],"version-history":[{"count":1,"href":"https:\/\/www.examlabs.com\/certification\/wp-json\/wp\/v2\/posts\/25084\/revisions"}],"predecessor-version":[{"id":25085,"href":"https:\/\/www.examlabs.com\/certification\/wp-json\/wp\/v2\/posts\/25084\/revisions\/25085"}],"wp:attachment":[{"href":"https:\/\/www.examlabs.com\/certification\/wp-json\/wp\/v2\/media?parent=25084"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.examlabs.com\/certification\/wp-json\/wp\/v2\/categories?post=25084"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.examlabs.com\/certification\/wp-json\/wp\/v2\/tags?post=25084"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}