{"id":19174,"date":"2026-09-22T12:04:36","date_gmt":"2026-09-22T12:04:36","guid":{"rendered":"https:\/\/www.examlabs.com\/certification\/?p=19174"},"modified":"2026-09-22T12:04:36","modified_gmt":"2026-09-22T12:04:36","slug":"pmi-cpmai-practice-test-questions-and-exam-dumps-part18-q341-360","status":"publish","type":"post","link":"https:\/\/www.examlabs.com\/certification\/pmi-cpmai-practice-test-questions-and-exam-dumps-part18-q341-360\/","title":{"rendered":"PMI CPMAI Practice Test Questions and Exam Dumps Part18 Q341-360"},"content":{"rendered":"

View Full <\/b>PMI CPMAI Exam Dumps<\/b><\/a> and Practice Test Dumps.<\/b><\/h2>\n

 <\/p>\n

Question 341<\/b><\/h3>\n

Which activity should be performed before selecting a machine-learning algorithm for an AI use case?<\/b><\/p>\n

    \n
  1. Determine the problem type, requirements, data characteristics, and constraints<\/span><\/li>\n
  2. Deploy the most complex algorithm immediately<\/span><\/li>\n
  3. Increase the training dataset without analysis<\/span><\/li>\n
  4. Choose the algorithm based only on popularity<\/span><\/li>\n<\/ol>\n

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

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

    Algorithm selection should follow an understanding of the actual problem, available data, expected outputs, performance requirements, and operational constraints. Teams should first determine whether the problem involves classification, regression, clustering, generation, or another type of task. They should also consider data volume, feature types, interpretability needs, latency, scalability, security, and available computing resources. Choosing an algorithm solely because it is popular or technically sophisticated may introduce unnecessary complexity. A structured evaluation of candidate approaches allows the team to compare alternatives against business and technical requirements. The selected algorithm should provide an appropriate balance between performance, maintainability, explainability, cost, and operational suitability.<\/span><\/p>\n

    Question 342<\/b><\/h3>\n

    Which document provides a structured record of identified project risks, their owners, and planned responses?<\/b><\/p>\n

      \n
    1. Product roadmap<\/span><\/li>\n
    2. Risk register<\/span><\/li>\n
    3. Data dictionary<\/span><\/li>\n
    4. Model card<\/span><\/li>\n<\/ol>\n

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

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

      A risk register is used to document identified risks and information needed to manage them throughout the project. Typical fields can include the risk description, probability, impact, owner, response strategy, status, triggers, and mitigation or contingency actions. Maintaining the register helps the project team monitor changing risk conditions rather than treating risk assessment as a one-time activity. In an AI project, risks may involve data quality, privacy, security, model performance, vendor dependencies, operational readiness, or compliance. The register should be updated when new risks emerge or existing risks change. It provides a common reference for stakeholders and supports consistent risk communication and escalation.<\/span><\/p>\n

      Question 343<\/b><\/h3>\n

      Which technique can help determine whether a model’s performance is stable across multiple subsets of available data?<\/b><\/p>\n

        \n
      1. Cross-validation<\/span><\/li>\n
      2. Encryption<\/span><\/li>\n
      3. Data retention<\/span><\/li>\n
      4. Role assignment<\/span><\/li>\n<\/ol>\n

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

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

        Cross-validation evaluates model performance across multiple training and validation partitions of the available data. In techniques such as k-fold cross-validation, the dataset is divided into several folds and the model is trained and evaluated repeatedly using different folds for validation. This can provide a more robust estimate of how the model may generalize than relying on a single split. Cross-validation is especially useful when the available dataset is limited, although the exact approach should reflect the data structure. It does not eliminate the need for a final independent test set when appropriate. Teams should also prevent leakage during preprocessing and partitioning so that the evaluation remains trustworthy.<\/span><\/p>\n

        Question 344<\/b><\/h3>\n

        Which action is most appropriate when a critical AI project assumption is proven to be incorrect?<\/b><\/p>\n

          \n
        1. Ignore the assumption and continue unchanged<\/span><\/li>\n
        2. Delete the original assumption<\/span><\/li>\n
        3. Assess the impact and update the project plan or risk response as necessary<\/span><\/li>\n
        4. Automatically terminate the entire project<\/span><\/li>\n<\/ol>\n

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

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

          When an important assumption becomes invalid, the project team should first assess its impact on scope, schedule, cost, quality, risk, and expected benefits. Depending on the findings, the team may need to update plans, revise requirements, adjust estimates, create a new risk response, or escalate the issue for a governance decision. Not every incorrect assumption requires project termination, but ignoring a significant change can lead to unrealistic plans and unexpected problems. Maintaining an assumptions log helps teams identify assumptions that require validation. A structured response ensures that project decisions are based on current evidence rather than continuing to rely on conditions that are no longer true.<\/span><\/p>\n

          Question 345<\/b><\/h3>\n

          Which practice can help protect an AI model from unauthorized access after deployment?<\/b><\/p>\n

            \n
          1. Applying authentication, authorization, and least-privilege access controls<\/span><\/li>\n
          2. Increasing the number of model features<\/span><\/li>\n
          3. Removing system logs<\/span><\/li>\n
          4. Increasing the model’s training epochs<\/span><\/li>\n<\/ol>\n

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

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

            Authentication and authorization controls help ensure that only approved users, applications, or services can access an AI model or its related interfaces. Least privilege limits each identity to the permissions required for its legitimate responsibilities. Additional measures such as encryption, network segmentation, secrets management, monitoring, and audit logging can strengthen protection. Access controls should cover both direct model access and supporting resources such as model artifacts, APIs, data stores, and deployment environments. Increasing model complexity or training epochs does not provide security against unauthorized access. Security requirements should be considered during architecture and deployment planning rather than added only after an AI system has entered production.<\/span><\/p>\n

            Question 346<\/b><\/h3>\n

            Which characteristic is important when evaluating whether an AI dataset is representative of the intended population?<\/b><\/p>\n

              \n
            1. The dataset reflects relevant characteristics and conditions of the population in which the model will operate<\/span><\/li>\n
            2. The dataset contains only the easiest examples<\/span><\/li>\n
            3. The dataset is always as small as possible<\/span><\/li>\n
            4. The dataset contains only historical records regardless of relevance<\/span><\/li>\n<\/ol>\n

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

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

              A representative dataset reflects the relevant characteristics, conditions, and variation of the population or operating environment where the AI system will be used. Poor representation can cause models to perform differently across groups or situations that were insufficiently represented during development. Teams should examine factors such as demographic characteristics where relevant, geographic coverage, time periods, usage patterns, and important operating conditions. Historical data may be useful, but it should not automatically be assumed to represent future conditions. Sampling strategies and domain expertise can help identify gaps. Representativeness should be evaluated in relation to the intended use case because the relevant population can differ significantly between applications.<\/span><\/p>\n

              Question 347<\/b><\/h3>\n

              Which practice is most useful for maintaining consistency between development, testing, and production environments?<\/b><\/p>\n

                \n
              1. Using controlled configuration and environment management<\/span><\/li>\n
              2. Changing settings manually without records<\/span><\/li>\n
              3. Removing deployment documentation<\/span><\/li>\n
              4. Allowing each environment to use unrelated dependencies<\/span><\/li>\n<\/ol>\n

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

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

                Controlled environment and configuration management helps ensure that development, testing, and production systems use known and appropriate versions of dependencies, settings, libraries, infrastructure, and other components. Differences between environments can create failures that are difficult to reproduce and may cause a model or application to behave differently after deployment. Configuration should be versioned and changes should be controlled where appropriate. Automation can further reduce manual inconsistencies. Environment management is particularly important for AI systems because model behavior may depend on software versions, preprocessing logic, hardware, runtime settings, and data pipelines. Consistency improves reproducibility and makes deployment and troubleshooting more reliable.<\/span><\/p>\n

                Question 348<\/b><\/h3>\n

                Which situation is most likely to indicate underfitting?<\/b><\/p>\n

                  \n
                1. Excellent training performance with poor test performance<\/span><\/li>\n
                2. Poor performance on both training and unseen data because the model is too simple<\/span><\/li>\n
                3. Perfect performance on every dataset<\/span><\/li>\n
                4. Performance that improves after adding representative data<\/span><\/li>\n<\/ol>\n

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

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

                  Underfitting occurs when a model is not sufficiently capable of capturing meaningful patterns in the data. A common indication is poor performance on both training and validation or test datasets. The model may be too simple, use insufficiently informative features, or have been constrained too strongly during development. Potential responses may include improving feature engineering, selecting a more appropriate model, adjusting relevant parameters, or providing better training data. Underfitting differs from overfitting, where training performance is strong but performance on unseen data is substantially weaker. Teams should use suitable evaluation metrics and diagnostic analysis before deciding how to address the problem rather than increasing model complexity automatically.<\/span><\/p>\n

                  Question 349<\/b><\/h3>\n

                  Which activity can help determine whether an AI project is financially justified?<\/b><\/p>\n

                    \n
                  1. Cost-benefit analysis<\/span><\/li>\n
                  2. Feature scaling<\/span><\/li>\n
                  3. Confusion-matrix construction<\/span><\/li>\n
                  4. Data anonymization<\/span><\/li>\n<\/ol>\n

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

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

                    Cost-benefit analysis compares expected project costs with anticipated benefits and helps determine whether an AI initiative may provide sufficient value to justify investment. Costs can include data preparation, development, infrastructure, licensing, staffing, integration, security, monitoring, maintenance, and retirement. Benefits may include improved productivity, reduced costs, increased revenue, faster decisions, improved quality, or other measurable outcomes. The analysis should account for uncertainty and important assumptions rather than presenting speculative benefits as guaranteed results. Financial feasibility is one component of overall project evaluation and should be considered alongside technical feasibility, operational readiness, risk, strategic alignment, and other factors that can influence whether an AI initiative is practical.<\/span><\/p>\n

                    Question 350<\/b><\/h3>\n

                    Which control helps ensure that only approved personnel can modify a production AI model?<\/b><\/p>\n

                      \n
                    1. Public access to the model repository<\/span><\/li>\n
                    2. Role-based permissions with controlled change approval<\/span><\/li>\n
                    3. Shared administrator credentials<\/span><\/li>\n
                    4. Unrestricted write access<\/span><\/li>\n<\/ol>\n

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

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

                      Role-based permissions restrict access according to defined responsibilities, while controlled change approval ensures that production modifications follow an established process. Together, these controls reduce the likelihood of unauthorized or accidental changes to important AI assets. Production model repositories should normally have restricted write permissions, authentication, audit logging, and versioning. Shared administrator credentials make accountability more difficult and can increase security risks. Unrestricted write access creates opportunities for unauthorized changes that may affect model behavior or system reliability. Access should follow least-privilege principles, with permissions periodically reviewed to ensure that users retain only the access necessary for their current responsibilities.<\/span><\/p>\n

                      Question 351<\/b><\/h3>\n

                      Which approach can help evaluate whether a generative AI system produces responses that are appropriate for its intended use?<\/b><\/p>\n

                        \n
                      1. Evaluating outputs against predefined quality, safety, factuality, and task-specific criteria<\/span><\/li>\n
                      2. Measuring only response length<\/span><\/li>\n
                      3. Assuming every generated response is correct<\/span><\/li>\n
                      4. Removing human review from high-risk applications<\/span><\/li>\n<\/ol>\n

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

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

                        Generative AI systems require evaluation that reflects the risks and objectives of their intended use. Teams can establish criteria for factual accuracy, relevance, completeness, harmful content, instruction following, consistency, privacy, and other application-specific requirements. Evaluation may use representative prompts, benchmark datasets, expert review, automated metrics, or combinations of these methods. Response length alone does not demonstrate quality. Important outputs may also require human validation, particularly when errors could have significant consequences. Evaluation should be repeated after major model, prompt, retrieval, or system changes because generative behavior can change when components are modified. Clear evaluation criteria provide evidence for determining whether the system is suitable for its intended application.<\/span><\/p>\n

                        Question 352<\/b><\/h3>\n

                        Which activity helps establish accountability for an AI system after deployment?<\/b><\/p>\n

                          \n
                        1. Defining clear ownership and operational responsibilities<\/span><\/li>\n
                        2. Allowing responsibility to remain unspecified<\/span><\/li>\n
                        3. Removing support procedures<\/span><\/li>\n
                        4. Assigning ownership only after an incident<\/span><\/li>\n<\/ol>\n

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

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

                          Clear ownership ensures that specific people or teams are responsible for important aspects of an AI system after deployment. Responsibilities may include model monitoring, data management, incident response, security, access control, user support, retraining, compliance activities, and change approval. Without defined ownership, issues may remain unresolved because stakeholders assume someone else is responsible. Ownership should be established before production operation and documented in appropriate governance materials. Different responsibilities can be distributed among several teams, but accountability should remain clear. Defined roles also make escalation easier because operational staff know whom to contact when performance degradation, security events, data problems, or other significant issues are identified.<\/span><\/p>\n

                          Question 353<\/b><\/h3>\n

                          Which method can help an organization compare actual AI project progress against the approved plan?<\/b><\/p>\n

                            \n
                          1. Progress tracking against documented milestones, deliverables, and baselines<\/span><\/li>\n
                          2. Changing the project baseline every week without approval<\/span><\/li>\n
                          3. Measuring only the number of emails sent<\/span><\/li>\n
                          4. Ignoring schedule and scope information<\/span><\/li>\n<\/ol>\n

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

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

                            Progress tracking compares actual project performance with approved plans, milestones, deliverables, schedules, and other relevant baselines. This allows project teams to identify deviations early and investigate whether corrective action is required. In an AI project, progress may depend on data readiness, model development, validation, infrastructure, integration, governance approvals, and user preparation. Tracking only activity volume does not necessarily show whether meaningful deliverables are being completed. Changes to the baseline should follow appropriate change-management procedures rather than being made simply to hide deviations. Effective progress reporting gives stakeholders an accurate view of project status and helps them make timely decisions when scope, schedule, resources, or risks change.<\/span><\/p>\n

                            Question 354<\/b><\/h3>\n

                            Which technique is most appropriate for identifying natural groupings within unlabeled data?<\/b><\/p>\n

                              \n
                            1. Clustering<\/span><\/li>\n
                            2. Regression<\/span><\/li>\n
                            3. Classification<\/span><\/li>\n
                            4. Supervised ranking<\/span><\/li>\n<\/ol>\n

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

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

                              Clustering is an unsupervised-learning technique used to identify groups or structures within data without requiring predefined labels. It can be useful for applications such as customer segmentation, document grouping, anomaly investigation, or exploratory analysis. Different clustering algorithms make different assumptions about the structure of groups, so the appropriate method depends on the data and objective. Teams should also evaluate whether the resulting groups are meaningful from a business or domain perspective rather than assuming that every mathematical cluster represents a useful category. Because clustering does not use known target labels, interpretation and validation often require domain expertise and additional analysis to determine whether the discovered patterns are actionable.<\/span><\/p>\n

                              Question 355<\/b><\/h3>\n

                              Which practice can reduce the risk of a model relying too heavily on a small number of highly correlated features?<\/b><\/p>\n

                                \n
                              1. Feature analysis and appropriate feature selection or dimensionality reduction<\/span><\/li>\n
                              2. Removing all validation data<\/span><\/li>\n
                              3. Increasing user permissions<\/span><\/li>\n
                              4. Disabling model monitoring<\/span><\/li>\n<\/ol>\n

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

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

                                Feature analysis can identify highly correlated or redundant variables that may provide overlapping information. Depending on the model and use case, teams may address this through feature selection, combining variables, regularization, or dimensionality-reduction techniques such as PCA. The appropriate approach depends on whether interpretability, predictive performance, computational efficiency, or other considerations are most important. Correlation alone does not prove that a feature should be removed because some models can handle correlated inputs reasonably well. Teams should evaluate the impact of feature changes using suitable validation methods. Careful feature management can improve efficiency, reduce redundancy, and sometimes make models easier to maintain and interpret.<\/span><\/p>\n

                                Question 356<\/b><\/h3>\n

                                Which activity should be performed when an AI system generates an unexpected production incident?<\/b><\/p>\n

                                  \n
                                1. Ignore the event if the model eventually recovers<\/span><\/li>\n
                                2. Follow the incident-response process, investigate the cause, and document corrective actions<\/span><\/li>\n
                                3. Delete system logs immediately<\/span><\/li>\n
                                4. Modify the model without investigation<\/span><\/li>\n<\/ol>\n

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

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

                                  A production incident should be handled through a defined incident-response process that prioritizes containment, investigation, recovery, and learning. The team should collect relevant evidence, determine the scope and impact, identify the root or contributing causes, and implement appropriate corrective actions. Depending on the incident, actions may include rollback, restricting functionality, increasing human oversight, correcting data, or addressing infrastructure or security weaknesses. Logs and other evidence should be preserved according to organizational requirements because they can support investigation. After recovery, the team should document lessons learned and consider preventive improvements. An unplanned model change without investigation can make the situation harder to understand and potentially create additional problems.<\/span><\/p>\n

                                  Question 357<\/b><\/h3>\n

                                  Which factor should be considered when determining the latency requirement of an AI application?<\/b><\/p>\n

                                    \n
                                  1. How quickly users or downstream systems need the model response<\/span><\/li>\n
                                  2. Only the model’s training duration<\/span><\/li>\n
                                  3. The number of pages in the project plan<\/span><\/li>\n
                                  4. The size of the project team<\/span><\/li>\n<\/ol>\n

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

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

                                    Latency represents the time required for an AI system to provide a response after receiving an input or request. The required latency depends on how the system is used. Interactive applications may require rapid responses, while batch-processing workflows may tolerate much longer processing times. Teams should consider user expectations, downstream system requirements, transaction volumes, infrastructure, model complexity, and service-level objectives. Optimizing for extremely low latency can increase infrastructure costs or require architectural compromises, so requirements should be based on actual business needs. Latency should also be monitored after deployment because changes in traffic, infrastructure, model size, or dependencies can affect real-world response times.<\/span><\/p>\n

                                    Question 358<\/b><\/h3>\n

                                    Which approach helps ensure that AI project stakeholders receive information appropriate to their roles?<\/b><\/p>\n

                                      \n
                                    1. Developing a stakeholder-specific communication plan<\/span><\/li>\n
                                    2. Sending identical technical reports to everyone<\/span><\/li>\n
                                    3. Avoiding communication until project closure<\/span><\/li>\n
                                    4. Allowing communication to occur without defined responsibilities<\/span><\/li>\n<\/ol>\n

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

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

                                      A stakeholder-specific communication plan identifies what information different stakeholders need, how frequently they need it, and the appropriate communication method. Executives may need information about business value, risks, milestones, and decisions, while technical teams may require detailed information about architecture, data, model performance, and implementation. Operational users may need guidance about workflows, limitations, and support procedures. Tailoring communication improves understanding and reduces unnecessary information overload. The plan should also identify responsible communicators and escalation paths. Communication needs may change during the project, so the plan should be reviewed periodically to ensure that important stakeholders continue receiving timely and relevant information.<\/span><\/p>\n

                                      Question 359<\/b><\/h3>\n

                                      Which activity can help determine whether an AI model should be retrained after deployment?<\/b><\/p>\n

                                        \n
                                      1. Reviewing monitored performance, data changes, and predefined retraining criteria<\/span><\/li>\n
                                      2. Retraining the model every day regardless of evidence<\/span><\/li>\n
                                      3. Ignoring production performance<\/span><\/li>\n
                                      4. Removing the original model version<\/span><\/li>\n<\/ol>\n

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

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

                                        Retraining decisions should be based on evidence such as declining performance, meaningful data drift, concept drift, changes in business requirements, or predefined lifecycle criteria. Automatically retraining every day may introduce unnecessary changes, increase costs, and create additional validation requirements. Conversely, ignoring performance can allow a degraded model to remain in production. Organizations should define monitoring thresholds and decision procedures that indicate when retraining should be considered. A new model should then undergo appropriate validation before deployment. Previous model versions should be preserved so that the organization can compare results and roll back if necessary. This creates a controlled feedback loop between production monitoring and model improvement.<\/span><\/p>\n

                                        Question 360<\/b><\/h3>\n

                                        Which principle is most important when designing an AI system that makes decisions with significant consequences for people?<\/b><\/p>\n

                                          \n
                                        1. Maximize automation regardless of risk<\/span><\/li>\n
                                        2. Remove all documentation to simplify operations<\/span><\/li>\n
                                        3. Use appropriate safeguards, human oversight, transparency, and accountability<\/span><\/li>\n
                                        4. Optimize only for computational speed<\/span><\/li>\n<\/ol>\n

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

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

                                          AI systems that can significantly affect people require safeguards proportionate to the potential consequences of errors or misuse. Depending on the application, these safeguards may include human oversight, transparency about system behavior and limitations, appropriate validation, security controls, privacy protections, monitoring, appeal or review mechanisms, and clearly assigned accountability. Maximizing automation without considering risk can create unacceptable outcomes, while computational efficiency alone does not demonstrate responsible operation. The appropriate controls depend on the use case and context. Teams should assess potential impacts before deployment and continue monitoring the system afterward because risks can change as data, users, workflows, and operating conditions evolve.<\/span><\/p>\n

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                                          View Full PMI CPMAI Exam Dumps and Practice Test Dumps.   Question 341 Which activity should be performed before selecting a machine-learning algorithm for an AI use case? Determine the problem type, requirements, data characteristics, and constraints Deploy the most complex algorithm immediately Increase the training dataset without analysis Choose the algorithm based only on […]<\/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\/19174"}],"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=19174"}],"version-history":[{"count":1,"href":"https:\/\/www.examlabs.com\/certification\/wp-json\/wp\/v2\/posts\/19174\/revisions"}],"predecessor-version":[{"id":19176,"href":"https:\/\/www.examlabs.com\/certification\/wp-json\/wp\/v2\/posts\/19174\/revisions\/19176"}],"wp:attachment":[{"href":"https:\/\/www.examlabs.com\/certification\/wp-json\/wp\/v2\/media?parent=19174"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.examlabs.com\/certification\/wp-json\/wp\/v2\/categories?post=19174"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.examlabs.com\/certification\/wp-json\/wp\/v2\/tags?post=19174"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}