{"id":18151,"date":"2026-09-22T05:40:35","date_gmt":"2026-09-22T05:40:35","guid":{"rendered":"https:\/\/www.examlabs.com\/certification\/?p=18151"},"modified":"2026-09-22T05:40:35","modified_gmt":"2026-09-22T05:40:35","slug":"databricks-certified-generative-ai-engineer-associate-practice-test-questions-and-exam-dumps-part15-q281-300","status":"publish","type":"post","link":"https:\/\/www.examlabs.com\/certification\/databricks-certified-generative-ai-engineer-associate-practice-test-questions-and-exam-dumps-part15-q281-300\/","title":{"rendered":"Databricks Certified Generative AI Engineer Associate Practice Test Questions and Exam Dumps Part15 Q281-300"},"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 281. Which approach is most appropriate for ensuring that retrieved documents remain relevant when a RAG application receives queries with different wording but the same intent?<\/b><\/p>\n

    \n
  1. <\/b> Disable semantic search<\/span>
    \n<\/span>2.<\/b> Use query rewriting or expansion<\/span>
    \n<\/span>3.<\/b> Remove document metadata<\/span>
    \n<\/span>4.<\/b> Increase model temperature<\/span><\/li>\n<\/ol>\n

    Correct Answer: 2. Query rewriting or expansion<\/b><\/p>\n

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

    Query rewriting or expansion can improve retrieval when users express the same intent using different terminology, sentence structures, or levels of detail. A rewriting step can transform the original request into a search-friendly representation while preserving its intended meaning. Expansion can also add related terms that improve the chance of matching relevant documents. This is particularly useful in enterprise RAG applications where users may use abbreviations, informal language, or business-specific terminology. The rewritten query is then sent to the retrieval system, allowing the vector or hybrid search process to locate more appropriate evidence before the language model generates its response.<\/span><\/p>\n

    Question 282. In a RAG application, why is metadata filtering useful before semantic retrieval results are passed to the language model?<\/b><\/p>\n

      \n
    1. <\/b> It increases the language model’s parameter count<\/span>
      \n<\/span>2.<\/b> It eliminates the need for embeddings<\/span>
      \n<\/span>3.<\/b> It restricts results according to attributes such as access, date, or document type<\/span>
      \n<\/span>4.<\/b> It automatically fine-tunes the foundation model<\/span><\/li>\n<\/ol>\n

      Correct Answer: 3. It restricts results according to attributes such as access, date, or document type<\/b><\/p>\n

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

      Metadata filtering allows a RAG system to apply structured constraints to retrieved information before that information becomes context for generation. Metadata might identify a department, document type, product version, geographic region, effective date, or authorization scope. Applying these filters can prevent irrelevant or outdated documents from entering the retrieval results and can also support access-control requirements. For example, an application could restrict retrieval to documents that a particular user is authorized to access. Combining metadata filters with semantic similarity often produces a more precise retrieval set than semantic similarity alone, especially in large enterprise knowledge bases containing many versions and categories of information.<\/span><\/p>\n

      Question 283. What is a major benefit of storing source identifiers with chunks in a generative AI knowledge base?<\/b><\/p>\n

        \n
      1. <\/b> They allow responses to be traced back to their source documents<\/span>
        \n<\/span>2.<\/b> They increase the context window of the model<\/span>
        \n<\/span>3.<\/b> They remove the need for retrieval<\/span>
        \n<\/span>4.<\/b> They guarantee that every generated answer is correct<\/span><\/li>\n<\/ol>\n

        Correct Answer: 1. They allow responses to be traced back to their source documents<\/b><\/p>\n

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

        Source identifiers provide traceability between retrieved chunks and the original documents from which those chunks were created. This information can be used to present citations, investigate incorrect responses, verify the evidence behind an answer, and support auditing requirements. In an enterprise RAG application, traceability is especially valuable because users may need to understand where an answer originated or confirm that the information is based on an approved source. Source identifiers do not guarantee factual correctness, but they make it easier to inspect the evidence and diagnose problems in the ingestion, retrieval, or generation pipeline.<\/span><\/p>\n

        Question 284. What should a RAG application do when retrieved documents provide conflicting information?<\/b><\/p>\n

          \n
        1. <\/b> Always select the longest document<\/span>
          \n<\/span>2.<\/b> Ignore all retrieved documents<\/span>
          \n<\/span>3.<\/b> Increase temperature to resolve the conflict<\/span>
          \n<\/span>4.<\/b> Use source authority, version, and effective-date information to determine which evidence should be used<\/span><\/li>\n<\/ol>\n

          Correct Answer: 4. Use source authority, version, and effective-date information to determine which evidence should be used<\/b><\/p>\n

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

          Conflicting information in a knowledge base should be handled using explicit evidence-selection rules rather than allowing the model to arbitrarily choose one source. Metadata such as document authority, publication date, effective date, version, or business ownership can help identify which source should take precedence. For example, a current approved policy may be more appropriate than an older policy document. The application can filter or rank sources based on these attributes before generation. If the conflict cannot be resolved reliably, the system should communicate the uncertainty instead of presenting an unsupported conclusion. This improves transparency and reduces the risk of misleading users.<\/span><\/p>\n

          Question 285. Which metric is particularly useful for determining whether a retriever returns most of the relevant documents available for a query?<\/b><\/p>\n

            \n
          1. <\/b> Retrieval recall<\/span>
            \n<\/span>2.<\/b> Token count<\/span>
            \n<\/span>3.<\/b> Model temperature<\/span>
            \n<\/span>4.<\/b> Generation latency<\/span><\/li>\n<\/ol>\n

            Correct Answer: 1. Retrieval recall<\/b><\/p>\n

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

            Retrieval recall measures how many of the relevant items that should have been retrieved were actually returned by the retrieval system. A low recall value means that important evidence is frequently being missed, even if the retrieved documents themselves appear relevant. This metric is useful when diagnosing retrieval failures in RAG systems because an answer cannot be grounded in a document that was never retrieved. Improving recall may involve changing the embedding model, increasing the retrieval candidate set, improving chunking, using query rewriting, or adding hybrid retrieval. Retrieval recall should generally be evaluated separately from final answer quality so that problems in different pipeline stages can be identified.<\/span><\/p>\n

            Question 286. Why can a reranker improve the quality of a RAG retrieval pipeline?<\/b><\/p>\n

              \n
            1. <\/b> It trains the foundation model during every request<\/span>
              \n<\/span>2.<\/b> It removes the need for a vector index<\/span>
              \n<\/span>3.<\/b> It reorders candidate documents using a more detailed relevance assessment<\/span>
              \n<\/span>4.<\/b> It automatically increases the context window<\/span><\/li>\n<\/ol>\n

              Correct Answer: 3. It reorders candidate documents using a more detailed relevance assessment<\/b><\/p>\n

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

              A reranker evaluates an initial set of retrieved candidates and reorders them according to their relevance to the user’s query. Initial vector retrieval is generally optimized for speed and can return several candidates that are only partially relevant. A reranker can perform a more detailed comparison between the query and candidate content, allowing the most useful evidence to move toward the top of the list. The application can then pass only the strongest results to the language model. This can improve context quality without requiring the entire retrieval system to be replaced. Reranking is particularly helpful when the knowledge base contains many semantically similar documents.<\/span><\/p>\n

              Question 287. What is a key reason to keep prompt templates under version control?<\/b><\/p>\n

                \n
              1. <\/b> It makes prompts impossible to change<\/span>
                \n<\/span>2.<\/b> It allows changes to be tracked and evaluated consistently<\/span>
                \n<\/span>3.<\/b> It eliminates the need for model evaluation<\/span>
                \n<\/span>4.<\/b> It automatically prevents hallucinations<\/span><\/li>\n<\/ol>\n

                Correct Answer: 2. It allows changes to be tracked and evaluated consistently<\/b><\/p>\n

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

                Version control makes it possible to identify exactly which prompt configuration was used during an experiment or production request. Prompt changes can significantly affect response quality, formatting, safety behavior, and grounding, even when the underlying model remains unchanged. By maintaining versions, teams can compare prompt variants using the same evaluation dataset and determine whether a change produces measurable improvement or regression. Versioning also makes rollback easier if a new prompt performs poorly in production. A mature generative AI workflow should therefore treat prompts as managed application artifacts rather than informal text that is changed without documentation or evaluation.<\/span><\/p>\n

                Question 288. Which design helps prevent unauthorized information from being included in RAG context?<\/b><\/p>\n

                  \n
                1. <\/b> Apply authorization-aware filtering before retrieval results are supplied to the model<\/span>
                  \n<\/span>2.<\/b> Increase the number of retrieved documents<\/span>
                  \n<\/span>3.<\/b> Disable metadata<\/span>
                  \n<\/span>4.<\/b> Use a higher temperature<\/span><\/li>\n<\/ol>\n

                  Correct Answer: 1. Apply authorization-aware filtering before retrieval results are supplied to the model<\/b><\/p>\n

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

                  Authorization-aware filtering ensures that users receive only information they are permitted to access. This control should be applied during retrieval or context assembly rather than relying solely on the language model to recognize restricted information after it has already been provided. Access-related metadata can identify ownership, department, role, document classification, or other permission attributes. The retrieval system can then restrict candidate documents accordingly. This is an important security principle because once sensitive information has been placed into model context, simply instructing the model not to disclose it may not provide sufficient protection. Authorization should therefore be enforced through application and data-layer controls.<\/span><\/p>\n

                  Question 289. What is the primary purpose of an evaluation baseline for a generative AI application?<\/b><\/p>\n

                    \n
                  1. <\/b> To guarantee production availability<\/span>
                    \n<\/span>2.<\/b> To replace monitoring<\/span>
                    \n<\/span>3.<\/b> To establish a reference point for measuring future changes<\/span>
                    \n<\/span>4.<\/b> To increase model context length<\/span><\/li>\n<\/ol>\n

                    Correct Answer: 3. To establish a reference point for measuring future changes<\/b><\/p>\n

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

                    An evaluation baseline provides a documented reference against which subsequent versions of a generative AI system can be compared. The baseline may include retrieval metrics, groundedness, response relevance, correctness, latency, cost, or other application-specific measurements. When a prompt, model, retrieval configuration, chunking strategy, or data source changes, the new configuration can be evaluated against the baseline using the same representative dataset. This makes improvement or regression easier to identify objectively. Without a baseline, teams may change multiple components and rely on subjective impressions. A stable baseline supports controlled experimentation, reproducibility, and evidence-based decisions during iterative development.<\/span><\/p>\n

                    Question 290. Why should an evaluation dataset contain representative queries from actual application use cases?<\/b><\/p>\n

                      \n
                    1. <\/b> To make every model produce identical answers<\/span>
                      \n<\/span>2.<\/b> To ensure the model has more parameters<\/span>
                      \n<\/span>3.<\/b> To eliminate the need for human evaluation<\/span>
                      \n<\/span>4.<\/b> To make evaluation results more relevant to expected production behavior<\/span><\/li>\n<\/ol>\n

                      Correct Answer: 4. To make evaluation results more relevant to expected production behavior<\/b><\/p>\n

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

                      A representative evaluation dataset should reflect the types of questions, terminology, difficulty levels, and information needs that users are expected to encounter in production. If evaluation data is unrelated to real usage, strong benchmark results may not translate into useful application performance. Representative queries can expose retrieval failures, missing knowledge, ambiguous terminology, poor response formatting, and grounding problems that generic tests might overlook. The dataset should ideally cover normal, difficult, and edge-case scenarios while remaining stable enough for meaningful comparisons. Using the same representative dataset across experiments also helps teams determine whether a new configuration genuinely changes performance.<\/span><\/p>\n

                      Question 291. What is a common trade-off when increasing the top-k value in retrieval?<\/b><\/p>\n

                      **1. It always decreases latency<\/span>
                      \n<\/span>**2. It can improve recall while also introducing more irrelevant context<\/span>
                      \n<\/span>**3. It removes the need for reranking<\/span>
                      \n<\/span>**4. It guarantees better answer quality<\/span><\/p>\n

                      Correct Answer: 2. It can improve recall while also introducing more irrelevant context<\/b><\/p>\n

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

                      The top-k setting controls how many retrieval results are selected for further processing. Increasing k can improve recall because more potentially relevant documents are included among the candidates. However, a larger result set can also introduce irrelevant or redundant information into the context passed to the language model. This can increase token usage, latency, and cost while potentially making it harder for the model to identify the most important evidence. Therefore, top-k should be tuned using evaluation data rather than increased indiscriminately. Combining an appropriate top-k value with metadata filtering and reranking can provide a better balance between recall and context quality.<\/span><\/p>\n

                      Question 292. What is the purpose of summarizing older conversation history in a multi-turn generative AI application?<\/b><\/p>\n

                        \n
                      1. <\/b> To preserve useful context while reducing the amount of conversation data sent to the model<\/span>
                        \n<\/span>2.<\/b> To increase the model’s parameter count<\/span>
                        \n<\/span>3.<\/b> To eliminate all previous user information<\/span>
                        \n<\/span>4.<\/b> To replace the retrieval system<\/span><\/li>\n<\/ol>\n

                        Correct Answer: 1. To preserve useful context while reducing the amount of conversation data sent to the model<\/b><\/p>\n

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

                        Conversation summarization helps a multi-turn application retain important information from earlier interactions without repeatedly sending the entire conversation history to the model. Long histories can consume significant context-window capacity, increase token costs, and add latency. A carefully generated summary can preserve key user requirements, decisions, preferences relevant to the task, and important facts while removing repetitive conversational details. The application can combine this summary with the most recent messages when constructing the next prompt. The summarization strategy should be evaluated because losing an important detail can negatively affect later responses. Effective history management therefore balances context retention, token efficiency, and response quality.<\/span><\/p>\n

                        Question 293. Which situation is most suitable for RAG rather than relying only on model pretraining?<\/b><\/p>\n

                          \n
                        1. <\/b> Teaching the model a permanent writing style only<\/span>
                          \n<\/span>2.<\/b> Changing the model’s general linguistic behavior<\/span>
                          \n<\/span>3.<\/b> Providing access to frequently updated organizational information<\/span>
                          \n<\/span>4.<\/b> Increasing the model’s parameter count<\/span><\/li>\n<\/ol>\n

                          Correct Answer: 3. Providing access to frequently updated organizational information<\/b><\/p>\n

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

                          RAG is particularly useful when an application needs to answer questions using information that changes frequently, such as company policies, product documentation, operational procedures, or current internal knowledge. Instead of retraining model weights whenever the information changes, the application can update its external knowledge source and retrieval index. Relevant information is retrieved at request time and supplied to the model as context. This approach can reduce the need for repeated model training and can make information updates easier to manage. Fine-tuning may still be appropriate for learned behaviors, specialized response patterns, or domain adaptation, but it is not generally a substitute for continuously changing factual knowledge.<\/span><\/p>\n

                          Question 294. What should be done when no retrieved document meets the application’s relevance threshold?<\/b><\/p>\n

                            \n
                          1. <\/b> Invent an answer from the model’s general knowledge<\/span>
                            \n<\/span>2.<\/b> Increase temperature until a response appears<\/span>
                            \n<\/span>3.<\/b> Ignore the retrieval result and continue normally<\/span>
                            \n<\/span>4.<\/b> Acknowledge insufficient evidence or request clarification<\/span><\/li>\n<\/ol>\n

                            Correct Answer: 4. Acknowledge insufficient evidence or request clarification<\/b><\/p>\n

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

                            When retrieval fails to produce sufficiently relevant evidence, the application should avoid presenting unsupported information as if it were grounded in the knowledge base. A relevance threshold can help identify situations where retrieved content is too weak to support a reliable answer. Depending on the application, the system can tell the user that the available information is insufficient, request a more specific query, or direct the request to another appropriate workflow. This behavior reduces hallucination risk and makes the system’s limitations more transparent. A model should not be encouraged to fabricate an answer simply because the retrieval stage failed to provide adequate supporting evidence.<\/span><\/p>\n

                            Question 295. Why is output schema validation useful when a generative AI application must return structured data?<\/b><\/p>\n

                              \n
                            1. <\/b> It verifies that generated output conforms to the required structure<\/span>
                              \n<\/span>2.<\/b> It guarantees factual correctness<\/span>
                              \n<\/span>3.<\/b> It eliminates all prompt engineering<\/span>
                              \n<\/span>4.<\/b> It increases retrieval recall<\/span><\/li>\n<\/ol>\n

                              Correct Answer: 1. It verifies that generated output conforms to the required structure<\/b><\/p>\n

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

                              Output schema validation checks whether a generated response follows the structural requirements expected by the consuming application. For example, an application may require fields such as a category, confidence value, summary, and source identifiers in a defined format. Even when a model is instructed to follow a schema, generated output can sometimes contain missing fields, incorrect data types, unexpected text, or malformed structures. Validation provides a programmatic checkpoint before the response is passed to downstream systems. It does not guarantee that the content is factually correct, but it improves reliability and interoperability by ensuring that structurally invalid responses can be rejected, repaired, or handled safely.<\/span><\/p>\n

                              Question 296. Which observability practice is most useful when investigating whether a poor answer was caused by retrieval or generation?<\/b><\/p>\n

                                \n
                              1. <\/b> Monitor only the final response text<\/span>
                                \n<\/span>2.<\/b> Record relevant retrieval and generation traces<\/span>
                                \n<\/span>3.<\/b> Disable application logs<\/span>
                                \n<\/span>4.<\/b> Change the model immediately<\/span><\/li>\n<\/ol>\n

                                Correct Answer: 2. Record relevant retrieval and generation traces<\/b><\/p>\n

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

                                Tracing allows developers to inspect the stages involved in producing a response, including the original query, rewritten query, retrieved documents, ranking results, prompt context, model configuration, and final output. This information helps separate retrieval problems from generation problems. For example, if the correct document was never retrieved, changing the language model may not address the underlying issue. If the correct evidence was retrieved but the model ignored or misinterpreted it, investigation can focus on prompt construction or generation behavior. Appropriate observability therefore provides evidence for troubleshooting instead of relying on assumptions. Sensitive information should still be handled carefully through privacy-aware logging practices.<\/span><\/p>\n

                                Question 297. What is a benefit of caching frequently reused embeddings or retrieval results?<\/b><\/p>\n

                                  \n
                                1. <\/b> It permanently changes the foundation model<\/span>
                                  \n<\/span>2.<\/b> It guarantees more accurate answers<\/span>
                                  \n<\/span>3.<\/b> It can reduce repeated computation, latency, and cost<\/span>
                                  \n<\/span>4.<\/b> It removes the need for authorization<\/span><\/li>\n<\/ol>\n

                                  Correct Answer: 3. It can reduce repeated computation, latency, and cost<\/b><\/p>\n

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

                                  Caching can improve application efficiency when the same computation is repeatedly requested and the underlying information has not changed. For example, embeddings for unchanged documents do not normally need to be recomputed every time a system processes a request. Similarly, carefully designed caching of retrieval results may reduce repeated search operations for identical or equivalent queries. These optimizations can reduce latency and computational cost. However, caching must consider data freshness, permissions, and invalidation. A cached result that becomes outdated or is served to an unauthorized user can create correctness or security problems. Therefore, caching should be implemented with appropriate expiration and access-control considerations.<\/span><\/p>\n

                                  Question 298. Why should a generative AI team retain the configuration of a previously validated deployment?<\/b><\/p>\n

                                    \n
                                  1. <\/b> To make future changes impossible<\/span>
                                    \n<\/span>2.<\/b> To provide a known configuration that can support rollback<\/span>
                                    \n<\/span>3.<\/b> To eliminate monitoring<\/span>
                                    \n<\/span>4.<\/b> To prevent evaluation<\/span><\/li>\n<\/ol>\n

                                    Correct Answer: 2. To provide a known configuration that can support rollback<\/b><\/p>\n

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

                                    Retaining a previously validated configuration provides a practical recovery option when a newly deployed version produces unexpected behavior. A configuration may include the model version, prompt version, retrieval settings, embedding model, index configuration, filtering rules, and other relevant application parameters. If a new version causes degraded quality, increased latency, unexpected output, or another production issue, the team can restore the known configuration while investigating the problem. Rollback is most effective when versions are documented and deployment artifacts are reproducible. This approach reduces recovery time and avoids trying to reconstruct an earlier working state from memory or incomplete records.<\/span><\/p>\n

                                    Question 299. Which practice best supports reproducible comparison of two RAG configurations?<\/b><\/p>\n

                                      \n
                                    1. <\/b> Change the evaluation dataset between every experiment<\/span>
                                      \n<\/span>2.<\/b> Compare configurations only through personal impressions<\/span>
                                      \n<\/span>3.<\/b> Evaluate both configurations using the same controlled dataset and metrics<\/span>
                                      \n<\/span>4.<\/b> Modify the model and prompt simultaneously without tracking versions<\/span><\/li>\n<\/ol>\n

                                      Correct Answer: 3. Evaluate both configurations using the same controlled dataset and metrics<\/b><\/p>\n

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

                                      Controlled evaluation requires a consistent basis for comparison. Using the same representative evaluation dataset and defined metrics allows teams to determine whether differences in results are associated with the configuration being tested rather than changes in the test conditions. Relevant metrics may include retrieval recall, retrieval precision, groundedness, response relevance, correctness, latency, and cost. Configuration details should also be recorded so the experiment can be reproduced. Changing several uncontrolled variables at once can make results difficult to interpret. A disciplined evaluation process therefore combines stable test data, explicit metrics, versioned artifacts, and documented experimental conditions.<\/span><\/p>\n

                                      Question 300. Which sequence best represents a reliable production RAG request lifecycle?<\/b><\/p>\n

                                        \n
                                      1. <\/b> Generate an answer, retrieve documents afterward, then validate the response<\/span>
                                        \n<\/span>2.<\/b> Increase temperature, skip retrieval, and rely on model memory<\/span>
                                        \n<\/span>3.<\/b> Retrieve everything, send all documents to the model, and ignore authorization<\/span>
                                        \n<\/span>4.<\/b> Validate the request, retrieve authorized relevant context, generate with grounding instructions, validate the output, and monitor the result<\/span><\/li>\n<\/ol>\n

                                        Correct Answer: 4. Validate the request, retrieve authorized relevant context, generate with grounding instructions, validate the output, and monitor the result<\/b><\/p>\n

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

                                        A reliable production RAG lifecycle should apply controls throughout the request rather than depending on the language model alone. The application first processes the request and applies appropriate authentication and authorization controls. It then retrieves relevant information using suitable search, filtering, and ranking techniques. The selected evidence is assembled into model context with instructions that encourage grounded generation. The resulting output can then be checked against required schemas, safety rules, or application constraints before being returned. Finally, monitoring and tracing provide visibility into performance and failures. This end-to-end lifecycle helps manage retrieval quality, security, reliability, latency, and maintainability across production requests.<\/span><\/p>\n

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

                                        View Full Databricks Certified Generative AI Engineer Associate Exam Dumps and Practice Test Dumps   Question 281. Which approach is most appropriate for ensuring that retrieved documents remain relevant when a RAG application receives queries with different wording but the same intent? Disable semantic search 2. Use query rewriting or expansion 3. Remove document metadata […]<\/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\/18151"}],"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=18151"}],"version-history":[{"count":1,"href":"https:\/\/www.examlabs.com\/certification\/wp-json\/wp\/v2\/posts\/18151\/revisions"}],"predecessor-version":[{"id":18152,"href":"https:\/\/www.examlabs.com\/certification\/wp-json\/wp\/v2\/posts\/18151\/revisions\/18152"}],"wp:attachment":[{"href":"https:\/\/www.examlabs.com\/certification\/wp-json\/wp\/v2\/media?parent=18151"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.examlabs.com\/certification\/wp-json\/wp\/v2\/categories?post=18151"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.examlabs.com\/certification\/wp-json\/wp\/v2\/tags?post=18151"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}