{"id":18124,"date":"2026-09-22T05:35:35","date_gmt":"2026-09-22T05:35:35","guid":{"rendered":"https:\/\/www.examlabs.com\/certification\/?p=18124"},"modified":"2026-09-22T05:35:35","modified_gmt":"2026-09-22T05:35:35","slug":"databricks-certified-generative-ai-engineer-associate-practice-test-questions-and-exam-dumps-part2-q21-40","status":"publish","type":"post","link":"https:\/\/www.examlabs.com\/certification\/databricks-certified-generative-ai-engineer-associate-practice-test-questions-and-exam-dumps-part2-q21-40\/","title":{"rendered":"Databricks Certified Generative AI Engineer Associate Practice Test Questions and Exam Dumps Part2 Q21-40"},"content":{"rendered":"

View Full <\/b>Databricks Certified Generative AI Engineer Associate Exam Dumps<\/b><\/a> and Practice Test Dumps<\/b><\/p>\n

 <\/p>\n

Question 21. Which Databricks component can be used to store and manage vector representations for similarity-based retrieval?<\/b><\/p>\n

    \n
  1. <\/b> Databricks SQL Dashboard<\/span><\/li>\n
  2. <\/b> Vector Search<\/span><\/li>\n
  3. <\/b> Notebook revision history<\/span><\/li>\n
  4. <\/b> Cluster policy<\/span><\/li>\n<\/ol>\n

    Correct Answer: 2. Vector Search<\/b><\/p>\n

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

    Databricks Vector Search is designed to support similarity-based retrieval using vector embeddings. In a generative AI application, documents or document chunks can be transformed into embeddings and indexed so that queries can retrieve semantically related content. This is a key component of many RAG architectures because the retrieved information can subsequently be supplied to a language model as context. Vector Search is focused specifically on finding relevant content based on vector similarity and can work with metadata and filtering requirements. A SQL dashboard is primarily used for visualization, notebook revision history manages notebook versions, and cluster policies govern compute configuration. Therefore, Vector Search is the relevant capability for semantic retrieval.<\/span><\/p>\n

    Question 22. What is the primary purpose of an embedding in a RAG application?<\/b><\/p>\n

      \n
    1. <\/b> To represent content as a numerical vector for similarity comparison<\/span><\/li>\n
    2. <\/b> To generate the final response directly<\/span><\/li>\n
    3. <\/b> To authenticate application users<\/span><\/li>\n
    4. <\/b> To replace the language model<\/span><\/li>\n<\/ol>\n

      Correct Answer: 1. To represent content as a numerical vector for similarity comparison<\/b><\/p>\n

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

      An embedding represents content such as text as a numerical vector that captures aspects of its semantic meaning. These vectors allow systems to compare queries and documents based on semantic similarity. In a RAG application, documents are usually processed into chunks and converted into embeddings before being indexed. When a user submits a question, the question can also be embedded, and the resulting vector can be compared with stored document vectors to identify relevant content. The embedding model does not normally generate the final natural-language answer. Instead, it supports the retrieval stage, which supplies useful context to the generative model. Good embedding selection and consistent embedding generation are important for retrieval quality.<\/span><\/p>\n

      Question 23. What is the purpose of metadata filtering during vector search?<\/b><\/p>\n

        \n
      1. <\/b> To increase the model’s temperature<\/span><\/li>\n
      2. <\/b> To remove the language model from the application<\/span><\/li>\n
      3. <\/b> To restrict retrieval to records that satisfy specified attributes<\/span><\/li>\n
      4. <\/b> To retrain the embedding model<\/span><\/li>\n<\/ol>\n

        Correct Answer: 3. To restrict retrieval to records that satisfy specified attributes<\/b><\/p>\n

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

        Metadata filtering allows a retrieval system to narrow search results according to structured attributes associated with indexed content. For example, an organization may store metadata identifying document type, department, language, date, or access-related information. A query can then use appropriate filters so that retrieval considers only records meeting the required conditions. This can improve relevance and can also support governance requirements when access restrictions are properly implemented. Metadata filtering is complementary to semantic similarity rather than a replacement for it. It does not change the language model’s temperature or retrain an embedding model. Combining semantic retrieval with suitable metadata filters can make RAG systems more precise and controlled.<\/span><\/p>\n

        Question 24. Why should a RAG application retrieve only relevant context rather than sending an entire knowledge base to the model?<\/b><\/p>\n

          \n
        1. <\/b> Entire knowledge bases cannot contain text<\/span><\/li>\n
        2. <\/b> Relevant context reduces unnecessary information and context-window usage<\/span><\/li>\n
        3. <\/b> The model cannot process any retrieved information<\/span><\/li>\n
        4. <\/b> Retrieval is only required during model training<\/span><\/li>\n<\/ol>\n

          Correct Answer: 2. Relevant context reduces unnecessary information and context-window usage<\/b><\/p>\n

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

          A RAG application typically retrieves a limited set of relevant documents or chunks instead of passing the entire knowledge base to the language model. Supplying focused context helps the model concentrate on information related to the user’s question and avoids consuming the available context window with irrelevant material. It can also reduce inference costs and improve response quality when retrieval is accurate. Sending large amounts of unrelated information can make it harder for the model to identify the most important evidence. Retrieval therefore acts as a selective information layer between the knowledge source and the generative model. Effective chunking and ranking are important for producing useful context.<\/span><\/p>\n

          Question 25. What is a common purpose of a system prompt in a generative AI application?<\/b><\/p>\n

            \n
          1. <\/b> To define high-level instructions and behavioral requirements<\/span><\/li>\n
          2. <\/b> To store vector embeddings permanently<\/span><\/li>\n
          3. <\/b> To create physical compute resources<\/span><\/li>\n
          4. <\/b> To replace application monitoring<\/span><\/li>\n<\/ol>\n

            Correct Answer: 1. To define high-level instructions and behavioral requirements<\/b><\/p>\n

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

            A system prompt can provide high-level instructions that establish how a generative model should behave within an application. It may define the model’s role, response requirements, boundaries, tone, formatting expectations, or rules for using retrieved context. In an enterprise RAG assistant, for example, the system prompt may instruct the model to answer based on supplied context and avoid inventing information when evidence is unavailable. System prompts are part of the application’s inference logic and are not intended to store embeddings or provision compute resources. Careful prompt design should be combined with evaluation because instructions alone do not guarantee that a model will always follow the desired behavior.<\/span><\/p>\n

            Question 26. Which approach can help evaluate whether generated answers are grounded in retrieved documents?<\/b><\/p>\n

              \n
            1. <\/b> Measuring cluster memory only<\/span><\/li>\n
            2. <\/b> Checking whether claims in the answer are supported by the retrieved context<\/span><\/li>\n
            3. <\/b> Increasing the number of database tables<\/span><\/li>\n
            4. <\/b> Disabling retrieval<\/span><\/li>\n<\/ol>\n

              Correct Answer: 2. Checking whether claims in the answer are supported by the retrieved context<\/b><\/p>\n

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

              Groundedness evaluation examines whether generated responses are supported by the information supplied to the model. In a RAG system, an evaluation process can compare statements in the generated answer against the retrieved documents or other approved evidence. If a response contains claims that cannot be supported by the available context, the system may have a grounding problem or the model may have introduced unsupported information. Groundedness is different from infrastructure monitoring because CPU or memory measurements do not determine whether an answer is factually supported. Evaluating groundedness helps teams understand whether the retrieval and generation components are working together effectively and whether additional safeguards are needed.<\/span><\/p>\n

              Question 27. What is the purpose of temperature tuning when developing a generative AI application?<\/b><\/p>\n

                \n
              1. <\/b> To control physical server temperature<\/span><\/li>\n
              2. <\/b> To change database storage capacity<\/span><\/li>\n
              3. <\/b> To influence the variability of model-generated responses<\/span><\/li>\n
              4. <\/b> To modify the embedding dimension automatically<\/span><\/li>\n<\/ol>\n

                Correct Answer: 3. To influence the variability of model-generated responses<\/b><\/p>\n

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

                Temperature influences the randomness or variability of token selection during text generation. Lower values generally encourage more predictable and deterministic responses, while higher values generally allow greater variation in the generated output. The appropriate setting depends on the application. A customer-support assistant that needs consistent answers may use a lower temperature, while a creative writing application may benefit from more variation. Temperature does not change the physical temperature of servers or automatically modify embedding dimensions. Developers should evaluate temperature using representative prompts and quality criteria rather than assuming that a particular value is universally appropriate. Other generation parameters can also affect the behavior of the final response.<\/span><\/p>\n

                Question 28. Why is prompt versioning useful in a production generative AI application?<\/b><\/p>\n

                  \n
                1. <\/b> It allows teams to track and reproduce changes to application instructions<\/span><\/li>\n
                2. <\/b> It guarantees perfect model accuracy<\/span><\/li>\n
                3. <\/b> It replaces source-control systems<\/span><\/li>\n
                4. <\/b> It prevents all model updates<\/span><\/li>\n<\/ol>\n

                  Correct Answer: 1. It allows teams to track and reproduce changes to application instructions<\/b><\/p>\n

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

                  Prompts are an important part of a generative AI application’s behavior, so changes to prompts should be tracked just like other application components. Prompt versioning allows teams to identify which instructions were used for a particular evaluation or production release. This improves reproducibility and makes it easier to investigate changes in output quality after a prompt update. Versioning does not guarantee perfect accuracy and does not eliminate the value of source control or other lifecycle-management practices. When prompts are treated as versioned artifacts, teams can evaluate different versions, roll back problematic changes, and maintain a clearer relationship between application releases and observed model behavior.<\/span><\/p>\n

                  Question 29. Which technique provides the model with examples of desired behavior directly within the prompt?<\/b><\/p>\n

                    \n
                  1. <\/b> Few-shot prompting<\/span><\/li>\n
                  2. <\/b> Vector indexing<\/span><\/li>\n
                  3. <\/b> Data partitioning<\/span><\/li>\n
                  4. <\/b> Cluster autoscaling<\/span><\/li>\n<\/ol>\n

                    Correct Answer: 1. Few-shot prompting<\/b><\/p>\n

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

                    Few-shot prompting provides the model with a small number of examples showing how inputs should correspond to desired outputs. These examples can help the model understand the expected task, response structure, classification categories, or formatting requirements without requiring changes to the model’s underlying weights. For instance, an application can provide several example questions and properly formatted answers before asking the model to process a new question. Few-shot prompting differs from vector indexing, which supports retrieval, and cluster autoscaling, which manages compute resources. The effectiveness of examples depends on their quality and relevance, so prompts containing examples should still be evaluated using representative inputs before being used in production.<\/span><\/p>\n

                    Question 30. What is the main purpose of an evaluation dataset for a generative AI application?<\/b><\/p>\n

                      \n
                    1. <\/b> To provide representative inputs for measuring application behavior<\/span><\/li>\n
                    2. <\/b> To replace production monitoring<\/span><\/li>\n
                    3. <\/b> To store only infrastructure logs<\/span><\/li>\n
                    4. <\/b> To permanently change model weights<\/span><\/li>\n<\/ol>\n

                      Correct Answer: 1. To provide representative inputs for measuring application behavior<\/b><\/p>\n

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

                      An evaluation dataset contains representative examples that can be used to assess how a generative AI application performs against defined requirements. Depending on the use case, examples may include user questions, expected responses, relevant source documents, safety cases, or other evaluation information. A well-designed dataset helps teams compare model or application versions consistently and identify regressions after changes to prompts, retrieval, models, or other components. The dataset does not replace production monitoring and does not permanently change model weights. Its primary role is to provide a controlled basis for measuring application behavior. Evaluation data should ideally represent realistic usage patterns and include challenging cases relevant to the application’s risks.<\/span><\/p>\n

                      Question 31. What does retrieval precision measure in a retrieval system?<\/b><\/p>\n

                        \n
                      1. <\/b> The proportion of retrieved results that are relevant<\/span><\/li>\n
                      2. <\/b> The total number of model parameters<\/span><\/li>\n
                      3. <\/b> The amount of available GPU memory<\/span><\/li>\n
                      4. <\/b> The number of users accessing the application<\/span><\/li>\n<\/ol>\n

                        Correct Answer: 1. The proportion of retrieved results that are relevant<\/b><\/p>\n

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

                        Retrieval precision measures the proportion of retrieved results that are relevant to the query. A retrieval system with high precision returns a relatively high percentage of useful results among the items it retrieves. This is important in RAG applications because irrelevant context can distract the generative model and potentially reduce response quality. Precision differs from recall, which considers how much of the relevant information available was successfully retrieved. Both measures can be useful because a system may retrieve many results while still missing important information or may return only a few results that are highly relevant. Retrieval evaluation should therefore consider metrics appropriate to the application’s requirements.<\/span><\/p>\n

                        Question 32. What is the main purpose of a reranker in a RAG retrieval pipeline?<\/b><\/p>\n

                          \n
                        1. <\/b> To replace the generative model<\/span><\/li>\n
                        2. <\/b> To reorder candidate results according to their relevance<\/span><\/li>\n
                        3. <\/b> To create cloud networking rules<\/span><\/li>\n
                        4. <\/b> To permanently store prompts<\/span><\/li>\n<\/ol>\n

                          Correct Answer: 2. To reorder candidate results according to their relevance<\/b><\/p>\n

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

                          A reranker is used after an initial retrieval stage to improve the ordering of candidate results. The first retrieval method may efficiently identify a broad set of potentially relevant documents or chunks. A reranking model can then examine the query and candidate content in greater detail and assign relevance scores, allowing the most useful results to be placed higher in the ranking. This can improve the context ultimately supplied to a generative model. A reranker does not replace the language model or manage cloud networking. It is a retrieval optimization component that can be particularly useful when semantic similarity alone does not provide sufficiently precise ordering for the application’s requirements.<\/span><\/p>\n

                          Question 33. Why can hybrid search be useful in a RAG application?<\/b><\/p>\n

                            \n
                          1. <\/b> It combines different retrieval approaches, such as keyword and semantic search<\/span><\/li>\n
                          2. <\/b> It disables all document retrieval<\/span><\/li>\n
                          3. <\/b> It guarantees that generated answers are correct<\/span><\/li>\n
                          4. <\/b> It removes the need for metadata<\/span><\/li>\n<\/ol>\n

                            Correct Answer: 1. It combines different retrieval approaches, such as keyword and semantic search<\/b><\/p>\n

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

                            Hybrid search combines multiple retrieval techniques to improve the chances of finding useful information. A common approach combines keyword-based search with vector-based semantic search. Keyword search can be effective when exact terms, identifiers, product names, or codes are important, while semantic search can identify conceptually related content even when wording differs. Combining the two approaches can provide more comprehensive retrieval for applications with varied query patterns. Hybrid search does not guarantee correct generated answers and does not eliminate the usefulness of metadata. The retrieved results still need to be evaluated for relevance and quality before being provided as context to the generative model.<\/span><\/p>\n

                            Question 34. What is the purpose of access control in an enterprise RAG application?<\/b><\/p>\n

                              \n
                            1. <\/b> To make every document publicly accessible<\/span><\/li>\n
                            2. <\/b> To ensure users and applications can access only authorized resources<\/span><\/li>\n
                            3. <\/b> To increase model temperature<\/span><\/li>\n
                            4. <\/b> To remove document metadata<\/span><\/li>\n<\/ol>\n

                              Correct Answer: 2. To ensure users and applications can access only authorized resources<\/b><\/p>\n

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

                              Access control helps ensure that users and applications can access only the data and AI resources for which they have appropriate permissions. This is particularly important for enterprise RAG systems because retrieved documents may contain confidential, personal, financial, operational, or otherwise restricted information. A secure design should consider permissions throughout the retrieval and generation workflow rather than simply protecting the final user interface. Metadata and identity information can support appropriate filtering, while centralized governance can help manage permissions consistently. Access control does not affect model temperature or make all documents public. It is a fundamental security and governance requirement for applications that retrieve information from organizational data sources.<\/span><\/p>\n

                              Question 35. What is a common advantage of using a managed model endpoint for a production AI application?<\/b><\/p>\n

                                \n
                              1. <\/b> The endpoint automatically writes all application code<\/span><\/li>\n
                              2. <\/b> The endpoint eliminates the need for testing<\/span><\/li>\n
                              3. <\/b> The endpoint provides a managed interface for scalable model inference<\/span><\/li>\n
                              4. <\/b> The endpoint permanently stores every user conversation<\/span><\/li>\n<\/ol>\n

                                Correct Answer: 3. The endpoint provides a managed interface for scalable model inference<\/b><\/p>\n

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

                                A managed model endpoint provides an interface through which applications can send inference requests to a deployed model. The managed service can handle aspects of serving infrastructure and scaling according to the capabilities and configuration of the platform. This can reduce the operational burden associated with maintaining model-serving infrastructure manually. However, using a managed endpoint does not eliminate the need for testing, monitoring, security controls, or application development. It also does not inherently mean that every user conversation is permanently stored. For production generative AI systems, managed serving is one part of a larger architecture that should include appropriate governance, evaluation, observability, and lifecycle management.<\/span><\/p>\n

                                Question 36. What is an important reason to log retrieval and generation information in a production RAG application?<\/b><\/p>\n

                                  \n
                                1. <\/b> To improve traceability and troubleshoot application behavior<\/span><\/li>\n
                                2. <\/b> To guarantee that every answer is correct<\/span><\/li>\n
                                3. <\/b> To increase the number of model parameters<\/span><\/li>\n
                                4. <\/b> To remove access controls<\/span><\/li>\n<\/ol>\n

                                  Correct Answer: 1. To improve traceability and troubleshoot application behavior<\/b><\/p>\n

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

                                  Logging useful application information can help teams understand how a RAG system behaves in production and investigate unexpected results. Depending on security and privacy requirements, logs may include request identifiers, model or prompt versions, retrieval information, latency measurements, errors, and other operational metadata. Such information can help developers determine whether an issue originated in retrieval, prompt construction, model inference, or application infrastructure. Logging does not guarantee correct answers and should be designed carefully so that sensitive information or credentials are not unnecessarily exposed. Appropriate logging therefore supports observability and troubleshooting while still requiring privacy, security, retention, and access-control considerations.<\/span><\/p>\n

                                  Question 37. Which change would most directly affect the information available to a RAG model without changing the model’s weights?<\/b><\/p>\n

                                    \n
                                  1. <\/b> Updating and reindexing the underlying knowledge source<\/span><\/li>\n
                                  2. <\/b> Replacing the application’s network cable<\/span><\/li>\n
                                  3. <\/b> Changing the database dashboard theme<\/span><\/li>\n
                                  4. <\/b> Increasing physical server temperature<\/span><\/li>\n<\/ol>\n

                                    Correct Answer: 1. Updating and reindexing the underlying knowledge source<\/b><\/p>\n

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

                                    A major advantage of RAG is that the application’s knowledge source can be updated without necessarily retraining or modifying the underlying language model’s weights. When new or revised documents become available, the documents can be processed, chunked, embedded, and indexed so that the retrieval system can find the updated information. This allows applications to respond using more current enterprise content while keeping the model itself unchanged. The effectiveness of the update depends on the indexing pipeline, embedding consistency, retrieval configuration, and source quality. Updating the knowledge base is therefore a practical way to change the information available to the application during inference without modifying the model’s learned parameters.<\/span><\/p>\n

                                    Question 38. What is the primary purpose of a guardrail that validates generated output against a required schema?<\/b><\/p>\n

                                      \n
                                    1. <\/b> To increase GPU memory<\/span><\/li>\n
                                    2. <\/b> To check whether the generated response conforms to expected structure<\/span><\/li>\n
                                    3. <\/b> To replace retrieval<\/span><\/li>\n
                                    4. <\/b> To train the foundation model<\/span><\/li>\n<\/ol>\n

                                      Correct Answer: 2. To check whether the generated response conforms to expected structure<\/b><\/p>\n

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

                                      Output validation can help ensure that a generated response follows the structure required by downstream software. For example, an application may expect a JSON object containing specific fields and data types. A schema-based guardrail can check whether the generated output conforms to those requirements and can trigger an error-handling or retry process when the response is invalid. This is particularly useful when generative AI is integrated into automated workflows. Output validation does not replace retrieval and does not train the underlying foundation model. It provides an additional control layer that makes generated content easier for software to consume safely and predictably. The validation rules should reflect the application’s actual requirements.<\/span><\/p>\n

                                      Question 39. What is the purpose of tracing in a generative AI application?<\/b><\/p>\n

                                        \n
                                      1. <\/b> To understand the sequence and performance of operations involved in a request<\/span><\/li>\n
                                      2. <\/b> To increase model context automatically<\/span><\/li>\n
                                      3. <\/b> To delete retrieved documents<\/span><\/li>\n
                                      4. <\/b> To permanently modify model weights<\/span><\/li>\n<\/ol>\n

                                        Correct Answer: 1. To understand the sequence and performance of operations involved in a request<\/b><\/p>\n

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

                                        Tracing provides visibility into the sequence of operations involved in processing an application request. In a RAG system, a trace can help show stages such as query processing, retrieval, prompt construction, model invocation, and response handling. This information can be useful when diagnosing latency problems, identifying failed components, and understanding where a response was generated from. Tracing does not automatically increase the model’s context window or modify model parameters. Proper tracing should also consider privacy and security because requests may contain sensitive information. When implemented appropriately, tracing improves observability by connecting individual operations into a coherent view of application behavior.<\/span><\/p>\n

                                        Question 40. Which approach best supports reliable iteration of a production generative AI application?<\/b><\/p>\n

                                          \n
                                        1. <\/b> Change multiple components without recording the changes<\/span><\/li>\n
                                        2. <\/b> Deploy every experiment directly to all users<\/span><\/li>\n
                                        3. <\/b> Version application artifacts, evaluate changes, monitor production behavior, and roll back when necessary<\/span><\/li>\n
                                        4. <\/b> Avoid testing because generative AI outputs are probabilistic<\/span><\/li>\n<\/ol>\n

                                          Correct Answer: 3. Version application artifacts, evaluate changes, monitor production behavior, and roll back when necessary<\/b><\/p>\n

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

                                          Reliable iteration requires controlled changes and evidence about their effects. Important artifacts can include application code, prompts, model versions, retrieval configurations, evaluation datasets, dependencies, and deployment settings. Versioning these components makes changes traceable and supports reproducibility. Evaluation before deployment helps identify regressions, while controlled rollout and production monitoring provide additional evidence after release. If a change causes unacceptable behavior, a documented rollback mechanism allows the application to return to a previously validated configuration. Because generative AI systems can behave differently across inputs, systematic testing and monitoring are particularly important. This lifecycle approach supports continuous improvement without sacrificing operational control.<\/span><\/p>\n

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

                                          View Full Databricks Certified Generative AI Engineer Associate Exam Dumps and Practice Test Dumps   Question 21. Which Databricks component can be used to store and manage vector representations for similarity-based retrieval? Databricks SQL Dashboard Vector Search Notebook revision history Cluster policy Correct Answer: 2. Vector Search Explanation: Databricks Vector Search is designed to support […]<\/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\/18124"}],"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=18124"}],"version-history":[{"count":2,"href":"https:\/\/www.examlabs.com\/certification\/wp-json\/wp\/v2\/posts\/18124\/revisions"}],"predecessor-version":[{"id":18126,"href":"https:\/\/www.examlabs.com\/certification\/wp-json\/wp\/v2\/posts\/18124\/revisions\/18126"}],"wp:attachment":[{"href":"https:\/\/www.examlabs.com\/certification\/wp-json\/wp\/v2\/media?parent=18124"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.examlabs.com\/certification\/wp-json\/wp\/v2\/categories?post=18124"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.examlabs.com\/certification\/wp-json\/wp\/v2\/tags?post=18124"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}