{"id":25078,"date":"2026-09-30T11:50:05","date_gmt":"2026-09-30T11:50:05","guid":{"rendered":"https:\/\/www.examlabs.com\/certification\/?p=25078"},"modified":"2026-09-30T11:50:05","modified_gmt":"2026-09-30T11:50:05","slug":"istqb-ct-genai-practice-test-questions-and-exam-dumps-part10-q181-200","status":"publish","type":"post","link":"https:\/\/www.examlabs.com\/certification\/istqb-ct-genai-practice-test-questions-and-exam-dumps-part10-q181-200\/","title":{"rendered":"ISTQB CT-GenAI Practice Test Questions and Exam Dumps Part10 Q181-200"},"content":{"rendered":"

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

 <\/p>\n

Question 181<\/b><\/h3>\n

A tester asks a GenAI assistant to generate test scenarios for a newly developed feature. Which information is most important to provide to improve the quality of the generated scenarios?<\/b><\/p>\n

    \n
  1. The tester’s preferred writing style<\/span><\/li>\n
  2. The approved requirements and relevant testing context<\/span><\/li>\n
  3. The model’s training history<\/span><\/li>\n
  4. The color scheme of the application<\/span><\/li>\n<\/ol>\n

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

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

    GenAI-generated test scenarios are strongly influenced by the information provided in the prompt and context. Supplying approved requirements, acceptance criteria, business rules, user roles, constraints, and relevant testing objectives gives the model a stronger basis for producing useful scenarios. Without this information, the model may rely on assumptions or generic patterns that do not match the actual system. Details such as application colors or the tester’s writing preferences may affect presentation but do not provide the essential testing context. Even with detailed input, generated scenarios should be reviewed against authoritative requirements to identify missing, incorrect, or irrelevant cases before they are used in formal testing activities.<\/span><\/p>\n

    Question 182<\/b><\/h3>\n

    Which GenAI risk occurs when generated content reflects unfair patterns present in the data or examples used by the model?<\/b><\/p>\n

      \n
    1. Tokenization<\/span><\/li>\n
    2. Regression<\/span><\/li>\n
    3. Bias<\/span><\/li>\n
    4. Compilation<\/span><\/li>\n<\/ol>\n

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

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

      Bias can occur when a GenAI system produces outputs that reflect undesirable patterns or imbalances present in its training data, examples, or supplied context. In software testing, biased outputs could cause certain user groups, scenarios, or conditions to receive insufficient attention. For example, generated test data might disproportionately represent one type of user while overlooking other relevant populations. Testers should therefore consider whether generated scenarios and data adequately represent the intended requirements and user groups. Reviewing outputs for inappropriate assumptions and comparing them with defined testing objectives can help identify potential bias. Tokenization and compilation are technical processes and do not describe this risk.<\/span><\/p>\n

      Question 183<\/b><\/h3>\n

      A tester wants GenAI to generate tests only for the password-reset functionality and not for other account features. Which prompt technique would help most?<\/b><\/p>\n

        \n
      1. Clearly define the scope and constraints<\/span><\/li>\n
      2. Remove all contextual information<\/span><\/li>\n
      3. Ask for unlimited creativity<\/span><\/li>\n
      4. Provide unrelated project documentation<\/span><\/li>\n<\/ol>\n

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

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

        Clearly defining scope and constraints helps prevent GenAI from generating content outside the intended testing objective. In this example, the prompt can specify that only password-reset functionality should be considered, while explicitly excluding unrelated account features. Additional context such as relevant requirements, supported reset methods, security rules, and expected behavior can further improve the output. Removing context may force the model to make assumptions, while unrelated documentation can introduce irrelevant information. A request for unlimited creativity does not establish boundaries. Scope constraints are especially useful when working with large systems where a broad prompt could produce excessive or unfocused test scenarios.<\/span><\/p>\n

        Question 184<\/b><\/h3>\n

        A GenAI model generates test cases that use an API endpoint that no longer exists. What is the most likely issue?<\/b><\/p>\n

          \n
        1. The model cannot generate API tests<\/span><\/li>\n
        2. The generated content may rely on outdated information<\/span><\/li>\n
        3. The tester used too many constraints<\/span><\/li>\n
        4. The model has performed successful execution<\/span><\/li>\n<\/ol>\n

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

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

          GenAI may generate technically plausible information based on outdated knowledge or outdated context. If an API endpoint has been removed or changed, a generated test case may still reference the old endpoint unless current API specifications are supplied and verified. The tester should compare generated tests with the current API documentation, interface definitions, and implementation. This illustrates why generated content should not automatically be treated as current or authoritative. Providing updated documentation can improve results, but the tester should still validate important details. The problem is not that GenAI cannot create API tests; rather, the generated information may no longer reflect the current system.<\/span><\/p>\n

          Question 185<\/b><\/h3>\n

          Which activity can GenAI support when reviewing software requirements for testability?<\/b><\/p>\n

            \n
          1. Automatically approving every requirement<\/span><\/li>\n
          2. Identifying ambiguous or incomplete statements for tester review<\/span><\/li>\n
          3. Replacing stakeholder discussions<\/span><\/li>\n
          4. Changing requirements without authorization<\/span><\/li>\n<\/ol>\n

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

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

            GenAI can assist requirement reviews by identifying wording that appears ambiguous, incomplete, inconsistent, or difficult to verify. For example, it may highlight terms such as \u201cfast,\u201d \u201ceasy,\u201d or \u201csecure\u201d and suggest that measurable acceptance criteria are needed. This can help testers prepare questions for analysts or stakeholders. However, GenAI should not automatically approve, modify, or replace requirements because requirements are governed by project stakeholders and established processes. Generated observations are suggestions that require human evaluation. The tester can use GenAI to accelerate the review process while maintaining traceability and ensuring that any actual requirement changes are formally discussed and approved.<\/span><\/p>\n

            Question 186<\/b><\/h3>\n

            A tester wants to use GenAI to create test data containing realistic but fictional customer information. What is this approach commonly intended to achieve?<\/b><\/p>\n

              \n
            1. Reduce unnecessary exposure of real customer information<\/span><\/li>\n
            2. Guarantee that all production defects will be reproduced<\/span><\/li>\n
            3. Eliminate the need for data validation<\/span><\/li>\n
            4. Make the application identical to production<\/span><\/li>\n<\/ol>\n

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

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

              Synthetic or fictional test data can provide realistic structures and values without directly exposing actual customer records. This can reduce privacy and security risks associated with using real personal information in test environments. For example, a tester may generate fictional names, addresses, account numbers, or transaction values that follow expected formats. However, synthetic data does not guarantee that every production scenario or defect will be reproduced. The generated data should also be reviewed to ensure that it satisfies the application’s validation rules and testing objectives. The main benefit is reducing unnecessary dependence on real sensitive records while still providing useful data for controlled testing.<\/span><\/p>\n

              Question 187<\/b><\/h3>\n

              A tester asks GenAI to explain why an automated test failed. The model proposes three possible causes. What should the tester do?<\/b><\/p>\n

                \n
              1. Treat all three causes as confirmed<\/span><\/li>\n
              2. Select the first explanation automatically<\/span><\/li>\n
              3. Verify the proposed causes using available evidence<\/span><\/li>\n
              4. Close the failed test immediately<\/span><\/li>\n<\/ol>\n

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

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

                GenAI can be useful for generating hypotheses about possible causes of test failures, especially when logs, stack traces, and error messages are supplied. However, proposed causes are not automatically confirmed facts. The tester should investigate the suggestions using available evidence such as logs, reproduction steps, source code, configuration, environment details, and recent changes. Multiple causes may be possible, and the actual problem could differ from every generated suggestion. Treating an AI-generated explanation as confirmed could lead to incorrect defect reports or wasted investigation effort. GenAI is therefore useful as an analysis assistant, while technical evidence and human judgment remain necessary to determine the actual cause.<\/span><\/p>\n

                Question 188<\/b><\/h3>\n

                Which prompt is most suitable for generating security-focused negative tests for a login function?<\/b><\/p>\n

                  \n
                1. \u201cWrite some tests.\u201d<\/span><\/li>\n
                2. \u201cGenerate only successful login scenarios.\u201d<\/span><\/li>\n
                3. \u201cDescribe the application.\u201d<\/span><\/li>\n
                4. \u201cGenerate negative tests for invalid credentials, account lockout, and unauthorized access according to the stated requirements.\u201d<\/span><\/li>\n<\/ol>\n

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

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

                  The fourth prompt provides a clear testing objective, specific security areas, and an appropriate negative-testing focus. Explicitly identifying invalid credentials, account lockout, and unauthorized access gives the model useful direction while keeping the scope focused. The tester can further improve the prompt by supplying approved requirements, expected responses, security constraints, and relevant user roles. A vague prompt may produce generic scenarios, while requesting only successful logins would omit important negative coverage. Generated security tests should still be reviewed because the model may miss important threats or suggest scenarios that do not match the application’s actual implementation or security requirements.<\/span><\/p>\n

                  Question 189<\/b><\/h3>\n

                  What is an important reason to keep records of prompts used to generate testing artifacts?<\/b><\/p>\n

                    \n
                  1. To increase the number of tokens in future responses<\/span><\/li>\n
                  2. To support traceability and understand how the artifact was produced<\/span><\/li>\n
                  3. To guarantee that the model will always respond identically<\/span><\/li>\n
                  4. To prevent testers from reviewing the output<\/span><\/li>\n<\/ol>\n

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

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

                    Recording prompts and relevant context can support traceability by showing how a GenAI-generated testing artifact was produced. This information can be useful when reviewing an artifact, investigating unexpected output, reproducing an activity, or understanding why a model generated a particular result. Depending on the AI tool and organizational governance, useful records may also include model information, important settings, input sources, and generated output. Keeping records does not guarantee identical future responses because GenAI can be non-deterministic and models can change. Documentation should therefore support transparency and investigation rather than being viewed as a guarantee of exact reproducibility.<\/span><\/p>\n

                    Question 190<\/b><\/h3>\n

                    A tester uses GenAI to generate a test execution summary from actual execution results. Which input should be considered authoritative for factual results?<\/b><\/p>\n

                      \n
                    1. The model’s general knowledge<\/span><\/li>\n
                    2. The tester’s preferred wording<\/span><\/li>\n
                    3. The verified execution data and test evidence<\/span><\/li>\n
                    4. A randomly generated example<\/span><\/li>\n<\/ol>\n

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

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

                      When generating a test execution summary, the actual verified execution data and supporting evidence should be the authoritative source for factual statements. GenAI can transform that information into a concise and readable report, but it should not invent or infer results that are not supported by evidence. For example, the number of passed and failed tests, known defects, and execution dates should come from reliable testing records. The tester should review the generated summary to ensure that the transformation did not introduce errors or omit important information. This approach combines GenAI’s documentation capabilities with evidence-based reporting and appropriate human oversight.<\/span><\/p>\n

                      Question 191<\/b><\/h3>\n

                      Which issue can arise when a GenAI model receives more context than it can effectively process or prioritize?<\/b><\/p>\n

                        \n
                      1. Context limitations may cause relevant information to be missed or underused<\/span><\/li>\n
                      2. The model automatically becomes a deterministic system<\/span><\/li>\n
                      3. All supplied information becomes equally authoritative<\/span><\/li>\n
                      4. Test execution is automatically completed<\/span><\/li>\n<\/ol>\n

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

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

                        GenAI systems have practical context limitations, and even when a large amount of information can technically be supplied, the model may not use every piece of information equally effectively. Important requirements can be overlooked when surrounded by large amounts of irrelevant, duplicated, outdated, or conflicting material. Testers should therefore provide focused and relevant context and clearly identify authoritative information. Organizing inputs can improve the usefulness of generated results. More context is not always better. The model does not automatically treat every supplied document as equally authoritative, and providing extensive information does not execute tests. Careful context management remains an important part of effective GenAI use.<\/span><\/p>\n

                        Question 192<\/b><\/h3>\n

                        A tester asks GenAI to convert a requirement into test cases and then requests the cases in JSON format. What is the second request primarily controlling?<\/b><\/p>\n

                          \n
                        1. The model’s training data<\/span><\/li>\n
                        2. The output structure<\/span><\/li>\n
                        3. The application’s runtime behavior<\/span><\/li>\n
                        4. The requirement’s business priority<\/span><\/li>\n<\/ol>\n

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

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

                          Requesting JSON format primarily controls the structure of the generated output. Structured output can be useful when testing artifacts need to be processed by another tool, imported into a repository, or reviewed consistently. The tester can specify fields such as test ID, description, preconditions, steps, and expected result. However, structured output does not guarantee that the information placed inside those fields is correct. The tester should still validate the generated content against requirements and testing objectives. The format request does not alter the model’s training data, application runtime behavior, or business priority unless those are separately specified in the prompt or system.<\/span><\/p>\n

                          Question 193<\/b><\/h3>\n

                          A tester asks GenAI to create tests for a payment system but does not mention currency, payment methods, or supported transaction limits. What is a likely consequence?<\/b><\/p>\n

                            \n
                          1. The model may make unsupported assumptions about the system<\/span><\/li>\n
                          2. The model will automatically retrieve all current business rules<\/span><\/li>\n
                          3. The model will refuse to create any test cases<\/span><\/li>\n
                          4. The generated tests will always be complete<\/span><\/li>\n<\/ol>\n

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

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

                            When important domain information is missing, GenAI may fill the gaps using assumptions based on general patterns or prior knowledge. For a payment system, currency, payment methods, transaction limits, authentication rules, and supported states can significantly affect the appropriate test conditions. If these details are not provided, generated tests may use unsupported assumptions and therefore be unsuitable for the actual system. The tester should provide relevant requirements and clarify missing information before relying on the output. GenAI does not automatically know every project-specific business rule. Reviewing assumptions is especially important for financial workflows where incorrect expectations can lead to significant testing gaps.<\/span><\/p>\n

                            Question 194<\/b><\/h3>\n

                            Which approach can help reduce hallucinations in GenAI-generated test documentation?<\/b><\/p>\n

                              \n
                            1. Ask the model to produce longer answers<\/span><\/li>\n
                            2. Provide authoritative source information and verify important generated claims<\/span><\/li>\n
                            3. Remove all requirements from the prompt<\/span><\/li>\n
                            4. Accept only responses containing technical terminology<\/span><\/li>\n<\/ol>\n

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

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

                              Providing authoritative source information gives the model a stronger factual basis for generating documentation. For example, approved requirements, verified execution results, current technical documentation, and defect records can provide reliable context. Important generated claims should still be checked against those sources because supplying context does not eliminate hallucination risk. Longer responses do not necessarily improve factual accuracy, and technical terminology can make an answer sound credible without making it correct. Removing requirements would usually make the model more dependent on assumptions. Combining reliable context with human verification is therefore a practical way to reduce the risk of inaccurate information entering testing documentation.<\/span><\/p>\n

                              Question 195<\/b><\/h3>\n

                              A tester wants to determine whether a GenAI-generated test suite contains duplicate scenarios. Which activity is most appropriate?<\/b><\/p>\n

                                \n
                              1. Compare test conditions and expected behavior across the generated cases<\/span><\/li>\n
                              2. Count the number of characters in each test<\/span><\/li>\n
                              3. Select the test with the longest description<\/span><\/li>\n
                              4. Remove all tests with similar names without review<\/span><\/li>\n<\/ol>\n

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

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

                                Duplicate or highly redundant test cases should be identified by examining their actual test conditions, inputs, behavior, and expected results rather than relying only on names or description length. Two tests may have different wording while exercising exactly the same behavior, while two similarly named tests may legitimately cover different conditions. The tester should compare the intended coverage and determine whether each case contributes distinct value. Removing tests automatically based on similar names could eliminate useful coverage. GenAI can help identify possible duplicates, but human review is useful for confirming whether scenarios are genuinely redundant and whether consolidation would affect traceability or risk coverage.<\/span><\/p>\n

                                Question 196<\/b><\/h3>\n

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

                                  \n
                                1. GenAI can suggest updates to automation when application changes affect tests<\/span><\/li>\n
                                2. GenAI guarantees that all updated scripts will execute successfully<\/span><\/li>\n
                                3. GenAI eliminates the need to inspect changed locators<\/span><\/li>\n
                                4. GenAI should modify production systems automatically<\/span><\/li>\n<\/ol>\n

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

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

                                  GenAI can assist with test automation maintenance by analyzing changed application elements, suggesting updated locators, rewriting affected code, or identifying tests that may need modification. This can reduce the effort involved in maintaining large automation suites. However, generated changes still require review and execution because the model may misunderstand the application change or produce incorrect code. Locator updates, assertions, dependencies, and test data should be checked against the current application. GenAI does not guarantee successful execution and should not be granted uncontrolled access to production systems. Human review and normal software-engineering practices remain important when incorporating AI-generated automation changes.<\/span><\/p>\n

                                  Question 197<\/b><\/h3>\n

                                  A tester receives two different valid-looking answers from GenAI for the same testing question. What should the tester consider?<\/b><\/p>\n

                                    \n
                                  1. Both answers are automatically correct<\/span><\/li>\n
                                  2. The longer answer must be correct<\/span><\/li>\n
                                  3. The difference may reflect probabilistic generation, so the answers should be evaluated against authoritative information<\/span><\/li>\n
                                  4. The model should always be trusted over project documentation<\/span><\/li>\n<\/ol>\n

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

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

                                    GenAI can produce different responses to the same or similar prompts because generative models are probabilistic and may be influenced by context, settings, or sampling. Different outputs do not automatically mean that one is correct or that both are correct. The tester should evaluate the responses against authoritative requirements, standards, technical documentation, system behavior, or other reliable evidence. Comparing outputs can sometimes reveal uncertainty or missing context and may help identify areas requiring clarification. Length and confidence are not reliable indicators of correctness. Project-specific documentation should generally provide the basis for testing decisions when it is the authoritative source.<\/span><\/p>\n

                                    Question 198<\/b><\/h3>\n

                                    Which practice is most appropriate when using GenAI to analyze confidential defect reports?<\/b><\/p>\n

                                      \n
                                    1. Upload all reports to any public AI service<\/span><\/li>\n
                                    2. Follow approved data-handling rules and use only authorized services<\/span><\/li>\n
                                    3. Remove all severity information before analysis<\/span><\/li>\n
                                    4. Share passwords so the model can understand the environment<\/span><\/li>\n<\/ol>\n

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

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

                                      Confidential defect reports may contain sensitive information such as customer details, internal architecture, security findings, credentials, or proprietary business information. Testers should therefore follow organizational data-handling policies and use only AI services that are authorized for the relevant type of information. Where appropriate, sensitive information can be minimized, masked, anonymized, or replaced with synthetic data. Uploading confidential reports to an unapproved service can create privacy, security, or intellectual-property risks. Passwords and other credentials should never be shared unnecessarily. GenAI can assist with defect analysis, but its use must remain consistent with the organization’s security and confidentiality requirements.<\/span><\/p>\n

                                      Question 199<\/b><\/h3>\n

                                      A tester asks GenAI to generate test cases from acceptance criteria and wants to identify which criteria are not covered. Which output would be most useful?<\/b><\/p>\n

                                        \n
                                      1. A list of unrelated testing concepts<\/span><\/li>\n
                                      2. A list containing only the longest test cases<\/span><\/li>\n
                                      3. A mapping between acceptance criteria and corresponding test cases<\/span><\/li>\n
                                      4. A summary containing no requirement references<\/span><\/li>\n<\/ol>\n

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

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

                                        Mapping acceptance criteria to corresponding test cases supports traceability and helps identify coverage gaps. Each criterion can be associated with one or more tests, allowing the tester to determine whether every required behavior has an appropriate verification. Criteria without corresponding tests can then be investigated as potential gaps. The mapping should be validated because GenAI may create incorrect requirement associations or fabricate identifiers. A list of unrelated concepts or long test cases does not provide useful coverage information. Removing requirement references would make traceability more difficult. Structured mapping is therefore a practical way to use GenAI for supporting requirement-based test design while retaining human validation.<\/span><\/p>\n

                                        Question 200<\/b><\/h3>\n

                                        What is the most appropriate overall approach when integrating GenAI into a software testing workflow?<\/b><\/p>\n

                                          \n
                                        1. Use GenAI for suitable tasks while applying validation, security, privacy, and human oversight<\/span><\/li>\n
                                        2. Replace all testing activities with generated content<\/span><\/li>\n
                                        3. Allow the model to make final decisions without review<\/span><\/li>\n
                                        4. Avoid defining any organizational rules for AI usage<\/span><\/li>\n<\/ol>\n

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

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

                                          GenAI can provide significant assistance across testing activities such as test design, automation, documentation, data generation, defect analysis, and exploratory testing. However, effective use requires appropriate controls. Generated content should be reviewed according to its risk, sensitive information should be handled according to privacy and security requirements, and important outputs should be validated against authoritative sources. Organizations should also establish suitable governance and define acceptable uses of GenAI. Replacing testing activities completely with generated content can introduce significant quality risks, while removing human oversight weakens accountability. A controlled approach combines AI-assisted productivity with established testing practices, security measures, and human judgment.<\/span><\/p>\n

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

                                          View Full ISTQB CT-GenAI Exam Dumps and Practice Test Dumps.   Question 181 A tester asks a GenAI assistant to generate test scenarios for a newly developed feature. Which information is most important to provide to improve the quality of the generated scenarios? The tester’s preferred writing style The approved requirements and relevant testing context […]<\/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\/25078"}],"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=25078"}],"version-history":[{"count":1,"href":"https:\/\/www.examlabs.com\/certification\/wp-json\/wp\/v2\/posts\/25078\/revisions"}],"predecessor-version":[{"id":25079,"href":"https:\/\/www.examlabs.com\/certification\/wp-json\/wp\/v2\/posts\/25078\/revisions\/25079"}],"wp:attachment":[{"href":"https:\/\/www.examlabs.com\/certification\/wp-json\/wp\/v2\/media?parent=25078"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.examlabs.com\/certification\/wp-json\/wp\/v2\/categories?post=25078"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.examlabs.com\/certification\/wp-json\/wp\/v2\/tags?post=25078"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}