{"id":25076,"date":"2026-09-30T11:49:48","date_gmt":"2026-09-30T11:49:48","guid":{"rendered":"https:\/\/www.examlabs.com\/certification\/?p=25076"},"modified":"2026-09-30T11:49:48","modified_gmt":"2026-09-30T11:49:48","slug":"istqb-ct-genai-practice-test-questions-and-exam-dumps-part9-q161-180","status":"publish","type":"post","link":"https:\/\/www.examlabs.com\/certification\/istqb-ct-genai-practice-test-questions-and-exam-dumps-part9-q161-180\/","title":{"rendered":"ISTQB CT-GenAI Practice Test Questions and Exam Dumps Part9 Q161-180"},"content":{"rendered":"

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

 <\/p>\n

Question 161<\/b><\/h3>\n

A tester asks GenAI to create test cases for a requirement and provides two examples of correctly formatted test cases. Which prompting technique is being used?<\/b><\/p>\n

    \n
  1. Zero-shot prompting<\/span><\/li>\n
  2. Few-shot prompting<\/span><\/li>\n
  3. Role prompting<\/span><\/li>\n
  4. Constraint removal<\/span><\/li>\n<\/ol>\n

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

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

    Few-shot prompting provides the GenAI model with a small number of examples that demonstrate the desired pattern or behavior. In this situation, the two existing test cases show the model how the tester expects new cases to be structured and written. The examples can improve consistency and help the model understand fields, wording, or formatting that may be difficult to describe through instructions alone. Zero-shot prompting would provide instructions without examples, while role prompting focuses on assigning a perspective or role. The tester should still review generated cases because examples guide the model but do not guarantee correctness, completeness, or alignment with every project requirement.<\/span><\/p>\n

    Question 162<\/b><\/h3>\n

    A GenAI tool generates several test cases that are almost identical. What should the tester do first?<\/b><\/p>\n

      \n
    1. Review the cases for redundancy and retain useful distinct coverage<\/span><\/li>\n
    2. Automatically add all duplicate cases to the test suite<\/span><\/li>\n
    3. Delete the complete generated output<\/span><\/li>\n
    4. Ask the model to generate unlimited additional cases<\/span><\/li>\n<\/ol>\n

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

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

      GenAI can generate redundant test cases, especially when the prompt asks for a large number of scenarios. The tester should examine the generated cases and determine whether they provide distinct test coverage or simply repeat the same conditions with minor wording changes. Useful unique scenarios can be retained while unnecessary duplicates can be removed or consolidated. Automatically adding every generated case can increase maintenance effort without improving coverage. Deleting all output may also discard valuable scenarios. Asking for unlimited additional cases could increase redundancy further. Reviewing generated content against existing coverage, requirements, and testing objectives helps ensure that the final suite remains efficient and meaningful.<\/span><\/p>\n

      Question 163<\/b><\/h3>\n

      Which factor can cause GenAI-generated testing information to become outdated even when the model produces a fluent response?<\/b><\/p>\n

        \n
      1. The response contains numbered options<\/span><\/li>\n
      2. The prompt contains a requirement ID<\/span><\/li>\n
      3. The model or its information source may not reflect recent changes<\/span><\/li>\n
      4. The tester uses a structured output format<\/span><\/li>\n<\/ol>\n

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

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

        GenAI systems may rely on model knowledge, supplied context, connected information sources, or retrieval mechanisms that do not necessarily reflect the latest changes. Software requirements, libraries, APIs, security practices, and organizational policies can change after the information available to the model was established. As a result, a fluent response may still contain outdated recommendations or assumptions. Testers should verify time-sensitive information against current authoritative sources. Providing recent project documentation can improve the context, but it does not eliminate the need for validation. Numbered options, requirement identifiers, and structured formatting affect presentation and traceability, but they do not directly solve the problem of outdated information.<\/span><\/p>\n

        Question 164<\/b><\/h3>\n

        A tester wants to use GenAI to identify possible test conditions from a user story. What should the prompt primarily communicate?<\/b><\/p>\n

          \n
        1. The tester’s preferred screen resolution<\/span><\/li>\n
        2. The intended testing objective and relevant user-story context<\/span><\/li>\n
        3. The maximum number of words in every explanation<\/span><\/li>\n
        4. The model’s internal architecture<\/span><\/li>\n<\/ol>\n

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

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

          A useful GenAI prompt should provide enough context for the model to understand what is being tested and what the tester wants to achieve. For a user story, this may include the story itself, acceptance criteria, relevant actors, business rules, and the requested testing objective. Clear context helps the model identify meaningful test conditions rather than producing generic suggestions. Screen resolution or response length may influence presentation but does not establish the testing objective. The model’s internal architecture is generally irrelevant to the task. Even with a well-structured prompt, the tester should compare generated conditions with the actual requirements and use professional judgment to determine whether important scenarios have been covered.<\/span><\/p>\n

          Question 165<\/b><\/h3>\n

          Which statement about GenAI-generated expected results is most accurate?<\/b><\/p>\n

            \n
          1. Expected results should be accepted whenever they sound reasonable<\/span><\/li>\n
          2. Expected results do not need to match requirements<\/span><\/li>\n
          3. Expected results should be validated against the expected system behavior<\/span><\/li>\n
          4. GenAI cannot generate expected results<\/span><\/li>\n<\/ol>\n

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

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

            Expected results are a critical part of a test case because they define the behavior against which actual results are compared. GenAI can help draft expected results, but the generated statements should be validated against approved requirements, specifications, business rules, or other authoritative sources. A plausible-sounding result may still be incorrect or may contain assumptions that are not supported by the system. Therefore, testers should verify important generated expectations before execution. GenAI is capable of generating expected results, but it should be treated as an assistant rather than an authoritative source of system behavior. Accurate expected results are essential for meaningful test evaluation and defect identification.<\/span><\/p>\n

            Question 166<\/b><\/h3>\n

            A tester gives GenAI a long document containing requirements, unrelated meeting notes, and obsolete specifications. What is a potential problem?<\/b><\/p>\n

              \n
            1. The model may use irrelevant or conflicting information when generating tests<\/span><\/li>\n
            2. The model will automatically identify the newest document with certainty<\/span><\/li>\n
            3. More information always guarantees more accurate output<\/span><\/li>\n
            4. The model will refuse to process all testing information<\/span><\/li>\n<\/ol>\n

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

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

              Providing excessive context does not automatically improve GenAI output. If the supplied material contains unrelated information, obsolete specifications, or conflicting requirements, the model may incorporate those details into its response. This can result in irrelevant or incorrect test cases. Testers should therefore provide focused, current, and authoritative context whenever possible. Important documents can be clearly identified by status, version, or purpose, and obsolete information should be excluded when it is not relevant. The model may not reliably determine which document is authoritative unless the prompt or system provides that information. Careful context management is therefore important for reliable AI-assisted test design.<\/span><\/p>\n

              Question 167<\/b><\/h3>\n

              Which activity is most appropriate when a tester uses GenAI to help analyze a failed automated test?<\/b><\/p>\n

                \n
              1. Allow the model to change production code automatically<\/span><\/li>\n
              2. Use the model to suggest possible causes and then verify them using evidence<\/span><\/li>\n
              3. Delete the failed test because the model identified a possible explanation<\/span><\/li>\n
              4. Mark the failure as a confirmed defect without investigation<\/span><\/li>\n<\/ol>\n

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

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

                GenAI can help testers analyze failures by summarizing logs, identifying patterns, suggesting possible causes, or proposing areas for investigation. However, generated explanations should be treated as hypotheses rather than confirmed root causes. The tester should verify suggestions using execution evidence, logs, source code, environment information, reproduction steps, and other appropriate evidence. Automatically changing production code or declaring a defect without investigation could introduce additional risk. Similarly, deleting a failed test does not resolve the underlying issue. A useful workflow combines GenAI’s ability to quickly explore possible explanations with human technical judgment and evidence-based investigation.<\/span><\/p>\n

                Question 168<\/b><\/h3>\n

                A tester needs GenAI to generate tests that must contain exactly five steps and one expected result. Which prompt element is most useful?<\/b><\/p>\n

                  \n
                1. A clear structural constraint<\/span><\/li>\n
                2. A request for creative writing<\/span><\/li>\n
                3. A request for unrelated examples<\/span><\/li>\n
                4. A statement that the model should decide the format<\/span><\/li>\n<\/ol>\n

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

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

                  A structural constraint explicitly defines the required format and limits of the generated output. By stating that every test must contain exactly five steps and one expected result, the tester provides clear guidance about how the response should be organized. Such constraints can improve consistency and make generated test cases easier to review or import into other tools. However, the tester should still verify that the generated steps and expected result are logically correct and sufficiently cover the intended scenario. Constraints guide the model’s output but do not guarantee quality. Clear instructions are therefore useful for controlling presentation while human review remains necessary for testing accuracy.<\/span><\/p>\n

                  Question 169<\/b><\/h3>\n

                  Which risk is associated with entering personally identifiable information into an unapproved GenAI service?<\/b><\/p>\n

                    \n
                  1. Improved requirement traceability<\/span><\/li>\n
                  2. Faster regression execution<\/span><\/li>\n
                  3. Potential unauthorized disclosure or processing of sensitive information<\/span><\/li>\n
                  4. Automatic improvement of test coverage<\/span><\/li>\n<\/ol>\n

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

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

                    Personally identifiable information can include names, addresses, identification numbers, contact details, financial information, or other data that can identify individuals. Entering such information into an unapproved GenAI service may expose it to unauthorized processing, retention, access, or disclosure depending on the service and its policies. Testers should follow organizational data-handling requirements and use appropriate anonymization, masking, synthetic data, or approved AI services when needed. GenAI can provide useful assistance with test data and analysis, but convenience should not override privacy and security requirements. The key risk is inappropriate exposure or processing of sensitive information rather than a testing-quality issue.<\/span><\/p>\n

                    Question 170<\/b><\/h3>\n

                    A tester asks GenAI to create negative tests for an API. Which type of scenario should the generated tests include?<\/b><\/p>\n

                      \n
                    1. Only valid requests with successful responses<\/span><\/li>\n
                    2. Invalid inputs, missing required fields, and unauthorized requests where applicable<\/span><\/li>\n
                    3. Only requests with maximum valid values<\/span><\/li>\n
                    4. Only performance measurements<\/span><\/li>\n<\/ol>\n

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

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

                      Negative testing examines how a system behaves when it receives invalid, unexpected, incomplete, or unauthorized input. For an API, useful negative scenarios can include missing required fields, malformed data, invalid values, unsupported methods, incorrect authentication, insufficient authorization, and other conditions defined by the API requirements. These scenarios help verify that the system handles invalid requests safely and predictably. Positive tests alone cannot provide this coverage. Performance testing addresses different objectives and may be performed separately. When using GenAI to generate negative tests, the tester should provide relevant API rules and security requirements and then validate that the generated scenarios are technically appropriate.<\/span><\/p>\n

                      Question 171<\/b><\/h3>\n

                      What is the main purpose of human review of GenAI-generated test artifacts?<\/b><\/p>\n

                        \n
                      1. To make every generated artifact longer<\/span><\/li>\n
                      2. To replace all automated testing<\/span><\/li>\n
                      3. To verify quality, correctness, relevance, and suitability before use<\/span><\/li>\n
                      4. To prevent GenAI from producing any output<\/span><\/li>\n<\/ol>\n

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

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

                        Human review provides an important quality-control step when GenAI is used in testing. Generated artifacts may contain incorrect assumptions, missing scenarios, irrelevant content, hallucinated information, security issues, or formatting problems. A tester can compare the output with requirements, risk information, existing coverage, technical constraints, and organizational policies. The purpose is not simply to make content longer or prevent AI use, but to determine whether the generated artifact is suitable for its intended purpose. The amount of review should reflect the risk and importance of the testing activity. Human oversight helps maintain accountability and reduces the chance that incorrect AI-generated information is accepted without verification.<\/span><\/p>\n

                        Question 172<\/b><\/h3>\n

                        A tester asks GenAI to convert manual test cases into automation code. Which additional step is essential?<\/b><\/p>\n

                          \n
                        1. Validate that the generated automation correctly implements the intended test behavior<\/span><\/li>\n
                        2. Assume the generated code has already been executed successfully<\/span><\/li>\n
                        3. Remove all assertions to avoid false failures<\/span><\/li>\n
                        4. Replace all test data with production data<\/span><\/li>\n<\/ol>\n

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

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

                          Converting manual tests into automation code involves translating test steps, conditions, data, and expected results into executable instructions. GenAI can accelerate this process, but the generated code may contain incorrect selectors, invalid syntax, unsuitable dependencies, incorrect assertions, or assumptions about the environment. The tester should therefore review and validate the generated automation against the original manual test case and the system under test. Appropriate execution in a controlled environment can provide additional evidence. Removing assertions reduces the effectiveness of the test, while production data may introduce unnecessary privacy and security risks. AI-generated automation should be treated as code requiring normal engineering and testing practices.<\/span><\/p>\n

                          Question 173<\/b><\/h3>\n

                          Which characteristic of a GenAI response indicates that it is directly addressing the requested testing task rather than an unrelated topic?<\/b><\/p>\n

                            \n
                          1. Relevance<\/span><\/li>\n
                          2. Token count<\/span><\/li>\n
                          3. Model temperature alone<\/span><\/li>\n
                          4. Training dataset size<\/span><\/li>\n<\/ol>\n

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

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

                            Relevance describes how well generated content addresses the requested task, context, and objective. In software testing, a relevant response should focus on the specified application, requirements, test scope, and requested type of testing. An answer may be technically sophisticated but still be unsuitable if it discusses unrelated functionality or assumptions. Token count describes the amount of processed or generated text, while temperature is a model-generation setting that can influence variability. Training dataset size is a model-development characteristic. None of these directly establishes whether the response addresses the tester’s actual objective. Relevance should therefore be considered when reviewing GenAI-generated testing content.<\/span><\/p>\n

                            Question 174<\/b><\/h3>\n

                            A tester notices that generated test cases contain assumptions about a feature that were never stated in the requirements. What should the tester do?<\/b><\/p>\n

                              \n
                            1. Treat every assumption as an approved business rule<\/span><\/li>\n
                            2. Identify and verify the assumptions before using the test cases<\/span><\/li>\n
                            3. Add the assumptions to production documentation automatically<\/span><\/li>\n
                            4. Ignore the assumptions because they were generated by AI<\/span><\/li>\n<\/ol>\n

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

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

                              GenAI may fill gaps in a prompt by making assumptions based on patterns learned from other information. These assumptions can be plausible but may not reflect the actual system or approved business rules. The tester should identify such assumptions and verify them against requirements, documentation, system behavior, or appropriate stakeholders. Unsupported assumptions should not automatically be incorporated into test cases or project documentation. Ignoring them can cause incorrect tests and misleading expected results, while automatically treating them as requirements can introduce unauthorized behavior. Reviewing assumptions is therefore an important part of validating AI-generated test artifacts and maintaining alignment with the actual test basis.<\/span><\/p>\n

                              Question 175<\/b><\/h3>\n

                              Which use of GenAI can help a tester perform exploratory testing more effectively?<\/b><\/p>\n

                                \n
                              1. Generating ideas for unusual scenarios and test charters<\/span><\/li>\n
                              2. Automatically declaring all generated ideas as defects<\/span><\/li>\n
                              3. Replacing all tester observation with generated text<\/span><\/li>\n
                              4. Preventing the tester from changing test ideas<\/span><\/li>\n<\/ol>\n

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

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

                                Exploratory testing involves learning, designing, and executing tests dynamically based on observations and available information. GenAI can support this activity by suggesting unusual scenarios, risk areas, questions, heuristics, test ideas, or exploratory test charters. These suggestions can help a tester consider perspectives that might otherwise be overlooked. However, generated ideas should not automatically be treated as defects or authoritative conclusions. The tester remains responsible for observing actual system behavior and deciding what to investigate. GenAI is most useful as a brainstorming and analysis assistant that expands the tester’s thinking while preserving human judgment and flexibility during exploratory testing.<\/span><\/p>\n

                                Question 176<\/b><\/h3>\n

                                What should a tester do if GenAI generates a test case with an invalid requirement identifier?<\/b><\/p>\n

                                  \n
                                1. Replace it with a guessed identifier<\/span><\/li>\n
                                2. Keep it because identifiers are not important<\/span><\/li>\n
                                3. Verify the identifier against the approved requirements and correct or remove it<\/span><\/li>\n
                                4. Create a new requirement automatically<\/span><\/li>\n<\/ol>\n

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

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

                                  Requirement identifiers provide traceability between requirements and testing artifacts, so an invalid identifier can create misleading documentation and make coverage analysis unreliable. The tester should verify the identifier against the approved requirement repository or other authoritative source. If the identifier is incorrect, it should be corrected or removed according to the project’s documentation practices. Guessing an identifier or creating a new requirement automatically could introduce false traceability. GenAI may generate identifiers that look realistic even when they do not exist, which is why generated metadata should be validated just like generated test steps or expected results. Accurate traceability supports reliable test management and reporting.<\/span><\/p>\n

                                  Question 177<\/b><\/h3>\n

                                  A tester wants consistent terminology in GenAI-generated test cases across a large project. Which approach is most useful?<\/b><\/p>\n

                                    \n
                                  1. Provide an approved glossary or terminology examples in the relevant context<\/span><\/li>\n
                                  2. Ask the model to use random synonyms<\/span><\/li>\n
                                  3. Remove domain terminology from the requirements<\/span><\/li>\n
                                  4. Generate each test case without any context<\/span><\/li>\n<\/ol>\n

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

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

                                    Providing an approved glossary, terminology rules, or representative examples can help GenAI use consistent language across generated test artifacts. This is especially useful in large projects where specific terms have defined meanings. Consistent terminology improves readability, reduces ambiguity, and supports communication between testers, developers, analysts, and other stakeholders. Asking the model to use random synonyms can introduce inconsistency, while removing domain terminology can make requirements less precise. Generating every case without context may result in different interpretations and wording. Even with an approved glossary, testers should review important outputs to ensure that terminology is used correctly and that generated content remains aligned with the project’s current documentation.<\/span><\/p>\n

                                    Question 178<\/b><\/h3>\n

                                    Which statement best describes the role of GenAI when creating a test summary report?<\/b><\/p>\n

                                      \n
                                    1. GenAI can draft the report from verified information, but the tester should review it<\/span><\/li>\n
                                    2. GenAI should invent missing execution results<\/span><\/li>\n
                                    3. GenAI should determine the final release decision independently<\/span><\/li>\n
                                    4. GenAI can replace all test evidence<\/span><\/li>\n<\/ol>\n

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

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

                                      GenAI can efficiently transform verified testing information into a readable test summary report. It can help organize execution results, summarize trends, describe completed activities, and adapt the language for different audiences. However, the source information should be accurate and the generated report should be reviewed before distribution. GenAI should not invent missing results or independently make important release decisions unless an organization has explicitly designed and governed an appropriate automated decision process. Test evidence remains important because summaries should be traceable to actual results. Human review helps ensure that the report accurately represents the testing performed, important risks, limitations, and outstanding issues.<\/span><\/p>\n

                                      Question 179<\/b><\/h3>\n

                                      A tester wants to compare two versions of GenAI-generated test suites. Which factor should be considered when determining whether one version provides useful improvement?<\/b><\/p>\n

                                        \n
                                      1. Whether the newer suite contains more words<\/span><\/li>\n
                                      2. Whether the newer suite improves relevant coverage without unacceptable quality or maintenance costs<\/span><\/li>\n
                                      3. Whether the newer suite uses more technical terminology<\/span><\/li>\n
                                      4. Whether the newer suite contains fewer requirement references<\/span><\/li>\n<\/ol>\n

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

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

                                        A larger generated test suite is not automatically better. When comparing two versions, the tester should consider whether the newer suite adds meaningful coverage of requirements, risks, boundaries, integrations, or other testing objectives while maintaining acceptable correctness and maintainability. Additional test cases that are redundant, irrelevant, or incorrect may increase maintenance effort without providing useful value. More words or technical terminology are not reliable indicators of testing quality. Requirement references generally support traceability, so reducing them would not necessarily indicate improvement. A balanced evaluation should consider coverage, quality, redundancy, execution effort, maintainability, and alignment with the actual testing objectives.<\/span><\/p>\n

                                        Question 180<\/b><\/h3>\n

                                        A tester uses GenAI to suggest improvements to an existing test suite. Which approach best preserves human accountability?<\/b><\/p>\n

                                          \n
                                        1. Allow the model to modify and approve the suite automatically<\/span><\/li>\n
                                        2. Accept every suggested change without review<\/span><\/li>\n
                                        3. Review the suggestions and approve changes using established testing criteria<\/span><\/li>\n
                                        4. Ignore all suggestions regardless of their potential value<\/span><\/li>\n<\/ol>\n

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

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

                                          Human accountability is preserved when testers use GenAI as an assistant and retain responsibility for evaluating and approving changes. Generated suggestions can help identify redundant cases, missing scenarios, opportunities for better organization, or potential maintenance improvements. However, each proposed change should be assessed against requirements, risks, coverage objectives, execution costs, and project standards. Automatically modifying and approving a test suite could introduce errors without appropriate oversight. Conversely, ignoring all AI assistance would prevent the team from benefiting from potentially useful suggestions. A controlled review and approval process allows GenAI to improve productivity while ensuring that final testing decisions remain evidence-based and aligned with established criteria.<\/span><\/p>\n

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

                                          View Full ISTQB CT-GenAI Exam Dumps and Practice Test Dumps.   Question 161 A tester asks GenAI to create test cases for a requirement and provides two examples of correctly formatted test cases. Which prompting technique is being used? Zero-shot prompting Few-shot prompting Role prompting Constraint removal Correct Answer: 2 Explanation Few-shot prompting provides 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\/25076"}],"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=25076"}],"version-history":[{"count":1,"href":"https:\/\/www.examlabs.com\/certification\/wp-json\/wp\/v2\/posts\/25076\/revisions"}],"predecessor-version":[{"id":25077,"href":"https:\/\/www.examlabs.com\/certification\/wp-json\/wp\/v2\/posts\/25076\/revisions\/25077"}],"wp:attachment":[{"href":"https:\/\/www.examlabs.com\/certification\/wp-json\/wp\/v2\/media?parent=25076"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.examlabs.com\/certification\/wp-json\/wp\/v2\/categories?post=25076"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.examlabs.com\/certification\/wp-json\/wp\/v2\/tags?post=25076"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}