CPMAI Praxisprüfung & CPMAI Examsfragen

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PMI CPMAI Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: AI System Testing and Evaluation- Model evaluation and monitoring
  • 1. Explainability and reliability assessment
    • 2. Performance evaluation and drift detection
      Topic 2: AI Model Development and Iteration- Model building and validation
      • 1. Machine learning / generative AI model development
        • 2. Iterative delivery approach
          Topic 3: Data Preparation for AI- Data cleaning and transformation
          • 1. Data quality assurance
            • 2. Feature engineering and preparation
              Topic 4: Identify Business Needs and Solutions26%- Problem framing and business alignment
              • 1. Define AI business objectives
                • 2. Feasibility and ROI analysis
                  Topic 5: AI Operationalization and Governance- Deployment and lifecycle management
                  • 1. AI governance and responsible AI
                    • 2. Continuous improvement and monitoring
                      Topic 6: Data for AI- Data identification and governance
                      • 1. Data sourcing and selection
                        • 2. Data compliance and ethics

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                          PMI Cognitive Project Management in AI (PMI-CPMAI) CPMAI Prüfungsfragen mit Lösungen (Q263-Q268):

                          263. Frage
                          Enhancing and cleaning data is an important action during which phase of CPMAI?

                          Antwort: E

                          Begründung:
                          Phase II of CPMAI is focused on data preparation, including enhancing and cleaning data to ensure quality input for model training.


                          264. Frage
                          A government agency is using an AI system to analyze public data for policymaking decisions.
                          The project manager needs to address risks related to data accuracy, privacy, and misuse. What represents the highest risk to the agency?

                          Antwort: A

                          Begründung:
                          Within PMI-CPMAI's "Support Responsible and Trustworthy AI Efforts," privacy and security are treated as core, high-severity risks because they can trigger regulatory violations, reputational damage, and harm to individuals. PMI explicitly calls out the need to establish a privacy/security plan with encryption and access controls, privacy impact assessments, and secure handling of personally identifiable information (PII) across the AI lifecycle. If user data is stored in an unsecured database, the agency faces immediate exposure to breach, unauthorized access, and misuse--risks that are typically higher impact than stale data, vendor reliance, or even lack of transparency. In PMI guidance on AI data life cycle management, prolonged retention and weak security increase breach likelihood over time, making insecure storage a critical vulnerability that undermines trust and compliance. While transparency gaps are serious (PMI also emphasizes explainability requirements and audit trails), a direct security failure that exposes user data is generally the most acute and consequential risk because it can cause harm quickly and irreversibly, and it can halt the program through legal and policy intervention.


                          265. Frage
                          Which of the following best describes the technical definition of Machine Learning?

                          Antwort: A


                          266. Frage
                          A government agency plans to implement a new AI-driven solution for automating risk analysis.
                          The project team needs to ensure that all stakeholders accept the solution and the project scope is well-defined. They must identify whether the AI approach is the best solution compared to traditional methods. Which method meets this objective?

                          Antwort: A

                          Begründung:
                          Performing a comprehensive AI go/no-go assessment helps determine whether an AI-driven solution is appropriate, feasible, and justified compared with traditional methods. It also supports stakeholder acceptance by clarifying scope, expected value, data readiness, technical suitability, and implementation risks before committing further.


                          267. Frage
                          A company plans to operationalize an AI solution. The project manager needs to ensure model performance is meeting selected thresholds before release. What is an effective way to confirm these thresholds before this release?

                          Antwort: C

                          Begründung:
                          Testing against validation datasets confirms whether the model meets the selected performance thresholds before release. This provides objective evidence that the model performs reliably on data separate from training data and is ready for operationalization.


                          268. Frage
                          ......

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