PMI-CPMAI™ Syllabus — Learning Objectives by Domain

Use this syllabus as your PMI-CPMAI™ coverage checklist. Work domain-by-domain and practice immediately after each task set.

What’s covered

• Support Responsible and Trustworthy AI Efforts (15%)
• Identify Business Needs and Solutions (26%)
• Identify Data Needs (26%)
• Manage AI Model Development and Evaluation (16%)
• Operationalize AI Solution (17%)

Support Responsible and Trustworthy AI Efforts (15%)

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Task 1 — Oversee privacy and security plan

• Establish data governance protocols for personally identifiable information (PII).
• Implement encryption and access controls for AI training data.
• Conduct privacy impact assessments for AI model deployment.
• Ensure compliance with GDPR, CCPA, and other data protection regulations.
• Design secure data handling procedures throughout the AI lifecycle.

Task 2 — Manage AI/ML transparency (e.g., data selection, algorithm selection)

• Document model selection criteria and decision rationale.
• Create transparent reporting on data sources and preprocessing steps.
• Establish explainability requirements for stakeholder communication.
• Maintain audit trails for algorithmic decision-making processes.
• Implement model interpretability tools and techniques.

Task 3 — Conduct bias checks (e.g., model, data, algorithm)

• Analyze training data for demographic and representation imbalances.
• Perform fairness testing across different population groups.
• Implement bias detection metrics and monitoring systems.
• Review model outputs for discriminatory patterns.
• Apply bias mitigation techniques during model development.

Task 4 — Monitor regulatory and policy compliance

• Track evolving AI regulations and industry standards.
• Ensure adherence to sector-specific compliance requirements.
• Coordinate with legal and compliance teams on AI governance.
• Implement compliance monitoring and reporting mechanisms.
• Maintain documentation for regulatory audits and reviews.

Task 5 — Manage accountability documentation and audit trail

• Create comprehensive records of AI model development decisions.
• Establish version control for models, data, and training processes.
• Document stakeholder approvals and go/no-go decision points.
• Maintain chain of custody records for training and test data.
• Prepare accountability reports for executive and regulatory review.

Identify Business Needs and Solutions (26%)

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Task 1 — Identify problem to be solved (e.g., needs, persona)

• Conduct stakeholder interviews to understand business pain points.
• Analyze existing processes to identify automation opportunities.
• Define target user personas and use cases for AI solutions.
• Map business problems to appropriate AI patterns and approaches.
• Validate problem statements with subject matter experts.

Task 2 — Evaluate initial AI feasibility

• Assess technical viability of proposed AI solutions.
• Analyze data availability and quality for model training.
• Evaluate computational resource requirements and constraints.
• Review organizational readiness for AI implementation.
• Compare AI approaches against traditional solution alternatives.

Task 3 — Conduct risk assessment(s) (e.g., security, safety, ethics)

• Identify potential failure modes and safety implications.
• Assess cybersecurity vulnerabilities in AI systems.
• Evaluate ethical implications of AI decision-making.
• Analyze reputational and business continuity risks.
• Develop risk mitigation strategies and contingency plans.

Task 4 — Develop AI project scope statement

• Define project boundaries and deliverables for AI initiatives.
• Establish success criteria and performance metrics.
• Identify in-scope and out-of-scope functionality.
• Document assumptions and constraints for AI implementation.
• Align scope with business objectives and resource availability.

Task 5 — Determine ROI

• Calculate expected benefits from AI solution implementation.
• Estimate total cost of ownership including infrastructure and maintenance.
• Develop business case with financial justification.
• Establish metrics for measuring return on investment.
• Create cost-benefit analysis for stakeholder decision-making.

Task 6 — Manage adoption/integration risks

• Assess organizational change management requirements.
• Identify potential user resistance and adoption barriers.
• Plan integration with existing systems and workflows.
• Develop training and communication strategies for end users.
• Monitor adoption metrics and address implementation challenges.

Task 7 — Draft AI solution

• Create high-level architecture for AI system design.
• Define data flow and processing requirements.
• Specify AI model types and algorithmic approaches.
• Document integration points with existing systems.
• Outline deployment and operational considerations.

Task 8 — Define success criteria (e.g., KPIs, metrics)

• Establish measurable performance indicators for AI models.
• Define business impact metrics and success thresholds.
• Create technical performance benchmarks and targets.
• Develop user satisfaction and adoption measurement criteria.
• Align success metrics with organizational objectives.

Task 9 — Support business case creation

• Gather financial data and projected benefits for business case.
• Collaborate with finance teams on cost estimates and projections.
• Develop compelling narratives for executive presentations.
• Provide technical expertise for business case validation.
• Review and refine business case documentation.

Task 10 — Identify project resources (e.g., people, hardware, contractors)

• Assess skill requirements for AI project team composition.
• Evaluate hardware and infrastructure needs for development and deployment.
• Identify gaps requiring external contractors or consultants.
• Plan resource allocation and timeline for project phases.
• Coordinate with procurement for specialized AI tools and platforms.

Identify Data Needs (26%)

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Task 1 — Define required data

• Specify data types and formats needed for AI model training.
• Determine data volume requirements and sampling strategies.
• Identify temporal and granularity requirements for data collection.
• Define data quality standards and acceptance criteria.
• Map data requirements to business objectives and use cases.

Task 2 — Identify data SMEs

• Locate domain experts with knowledge of relevant data sources.
• Engage business users who understand data context and meaning.
• Connect with data stewards and data governance teams.
• Identify technical experts familiar with data systems and structures.
• Establish communication channels with identified subject matter experts.

Task 3 — Identify data sources and locations

• Map internal databases and data warehouses containing relevant information.
• Explore external data sources and third-party data providers.
• Assess cloud storage and distributed data repositories.
• Inventory legacy systems and historical data archives.
• Document data ownership and access permissions.

Task 4 — Coordinate AI workspace and infrastructure

• Provision computing resources for data processing and model training.
• Establish secure development environments for AI teams.
• Configure data storage and backup systems for project needs.
• Set up collaboration tools and version control systems.
• Ensure compliance with security and governance requirements.

Task 5 — Gather required data

• Execute data extraction from identified sources and systems.
• Coordinate data transfers and migrations to AI development environments.
• Implement data collection processes for ongoing data feeds.
• Validate data completeness and accuracy during collection.
• Establish data refresh and update procedures.

Task 6 — Check data privacy, compliance, and access

• Verify data usage rights and licensing agreements.
• Ensure compliance with data protection regulations and policies.
• Implement access controls and user permissions for data resources.
• Conduct privacy impact assessments for data usage.
• Document data lineage and usage for audit purposes.

Task 7 — Oversee data evaluation

• Assess data quality dimensions including accuracy, completeness, and consistency.
• Analyze data distributions and identify potential biases or gaps.
• Evaluate data freshness and relevance for AI model training.
• Review data schema and structure for modeling compatibility.
• Conduct exploratory data analysis to understand data characteristics.

Task 8 — Determine if data meets solution needs

• Compare available data against defined requirements and specifications.
• Assess data sufficiency for training robust AI models.
• Identify data gaps and develop strategies for addressing deficiencies.
• Validate data representativeness for target use cases.
• Make go/no-go decisions based on data readiness assessment.

Task 9 — Convey data understanding to leadership

• Prepare executive summaries of data assessment findings.
• Create visualizations and reports to communicate data insights.
• Present data readiness status and recommendations to stakeholders.
• Translate technical data concepts into business-relevant language.
• Provide regular updates on data preparation progress and challenges.

Manage AI Model Development and Evaluation (16%)

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Task 1 — Oversee AI/ML model technique(s) (e.g., algorithm, selection)

• Research and evaluate appropriate algorithms for specific use cases.
• Guide selection between supervised, unsupervised, and reinforcement learning approaches.
• Assess trade-offs between model complexity, performance, and interpretability.
• Coordinate with data scientists on model architecture decisions.
• Review algorithm selection criteria and decision documentation.

Task 2 — Oversee AI/ML model QA/QC (e.g., configuration management, model performance)

• Establish model testing protocols and quality assurance procedures.
• Implement configuration management for model versions and parameters.
• Monitor model performance metrics during development and testing.
• Coordinate peer reviews and technical validation of model designs.
• Ensure adherence to coding standards and best practices.

Task 3 — Manage AI/ML model training

• Plan training schedules and resource allocation for model development.
• Monitor training progress and computational resource utilization.
• Coordinate hyperparameter tuning and optimization activities.
• Oversee cross-validation and model selection processes.
• Manage training data versioning and experiment tracking.

Task 4 — Manage data transformation to conduct data preparation

• Oversee data cleaning and preprocessing workflows.
• Coordinate feature engineering and selection activities.
• Manage data normalization and standardization processes.
• Supervise data augmentation and synthetic data generation.
• Ensure data transformation reproducibility and documentation.

Task 5 — Verify data quality for go/no-go decision to conduct data preparation

• Conduct final data quality assessments before model training.
• Validate data preprocessing and transformation results.
• Assess data representativeness and potential bias issues.
• Make decisions on data readiness for model development.
• Document data quality findings and recommendations.

Task 6 — Verify model ready for operationalization go/no-go decision

• Evaluate model performance against established success criteria.
• Assess model robustness and generalization capabilities.
• Review deployment readiness including infrastructure requirements.
• Validate model documentation and operational procedures.
• Make final approval decisions for model deployment.

Operationalize AI Solution (17%)

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Task 1 — Manage creation of AI solution deployment plan

• Develop comprehensive deployment strategy and timeline.
• Plan infrastructure requirements and resource allocation.
• Coordinate with IT teams on system integration and deployment.
• Establish rollback procedures and contingency plans.
• Create deployment checklists and validation criteria.

Task 2 — Manage AI solution deployment

• Coordinate deployment activities across technical teams.
• Monitor deployment progress and resolve implementation issues.
• Validate system functionality and performance in production environment.
• Manage user access provisioning and security configurations.
• Conduct post-deployment verification and testing.

Task 3 — Oversee model governance

• Establish model lifecycle management procedures.
• Implement model versioning and change control processes.
• Monitor model performance and drift detection.
• Coordinate model updates and retraining schedules.
• Ensure compliance with governance policies and standards.

Task 4 — Oversee AI solution metrics (e.g., KPI, model performance)

• Implement monitoring dashboards for business and technical metrics.
• Track key performance indicators and success measures.
• Analyze model performance trends and degradation patterns.
• Generate regular performance reports for stakeholders.
• Establish alerting systems for performance threshold breaches.

Task 5 — Prepare final report/lessons learned

• Document project outcomes and achievement of objectives.
• Capture lessons learned and best practices for future projects.
• Analyze what worked well and areas for improvement.
• Create knowledge transfer documentation for operational teams.
• Present final project results to stakeholders and leadership.

Task 6 — Manage AI solution transition plan

• Plan transition from project team to operational support.
• Coordinate knowledge transfer to production support teams.
• Establish ongoing maintenance and support procedures.
• Define roles and responsibilities for operational phase.
• Create handover documentation and training materials.

Task 7 — Oversee AI solution contingency plan

• Develop incident response procedures for AI system failures.
• Plan backup and disaster recovery strategies.
• Establish escalation procedures for critical issues.
• Create business continuity plans for AI service disruptions.
• Test and validate contingency procedures regularly Completion of the PMI-CPMAI Exam Prep Course is required to take the PMI-CPMAI exam. The PMI-CPMAI certification exam is comprised of 120 total questions . Of the 120 questions, 20 are considered pre-test questions. Pre-test questions do not affect the score and are used in examinations as an effective and legitimate way to test the validity of future examination questions. All questions are placed throughout the examination randomly. No. of Scored Questions No. of Pretest (Unscored) Questions Total Examination Questions 100 20 120 T he allotted time to complete the computer-based and online-proctored exam is two hours and forty minutes . Total Time Allotted 160 minutes It m ay take some candidates less than the allotted time to complete the exam. F or the PMI-CPMAI exam, there are no scheduled breaks. The exam is preceded by a tutorial and followed by a survey, both of which can take up to 15 minutes to complete. The time used to complete the tutorial and survey is not counted against total testing time. T he PMI-CPMAI exam is offered in English and will be offered in the following languages in January 202 6: Arabic, Brazilian Portuguese, French, German, Japanese, Korean, Simplified & Traditional Chinese, and Spanish. MI-CPMAI EXAM ELIGIBILITY Completion of the PMI-CPMAI Exam Prep Course is required to schedule and take the PMI-CPMAI exam . PMI-CPMAI requires no prior project management , technical , or AI experience or certifications to enroll in the course and take the exam. However, project or product management and AI fundamental knowledge are valuable . The course and exam cover the CPMA I (Cognitive Project Management in AI) Methodology and build knowledge tailored to managing AI initiatives from a project management perspective. Upon completion of the PMI-CPMAI Exam Prep course, individuals will be able to schedule and take their exa m via Pearson Vue . REGIST ERING FOR THE EXAM To purchase and register for the PMI Certified Professional in Managing AI (PMI-CPMAI) Exam Prep Course & Certification, please use the online certification system (myPMI ) to login into your myPMI account or if you do not yet have a PMI account, y ou can create a free account on PMI.org . A fter completing the PMI Certified Professional in Managing AI (PMI-CPMAI ) Exam Prep Course , you will be asked to provide your e xam details and schedule your exam by accessing your myPMI dashboard . Pearson VUE provides options to either take a proctored online exam or in-person exam at one of Pearson VUE’s testing centers. Official exam results will be available on your myPMI dashboard. Although PMI will email you reminders during the process, you have the responsibility to complete the course to schedule and take the exam . B efore registering for your exam , you will be required to read and agree to the PMI Code of Ethics and Professional Conduct, which can be found in the PMI Certification Handbook and on PMI.org. Y ou can also use the online certification system to:.
• D ownload your exam reports with pass/fail status.
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• Access your certification record and update your contact information.
• View your listing on the PMI Certification Registry The PMI-CPMAI exam is available to take in person (recommended) via computer-based test (CBT) at a test center or proctored online through our test delivery provider, Pearson Vue. Online proctored exams will require system tests and an extensive check-in process. Please allow for time prior to your exam to ensure you complete these processes. You have one year (12 months) from the time of purchase to obtain the certification.
• F or in person test center and availability (recommended) please make sure to review test centers near you by visiting: https://www.pearsonvue.com/us/en/pmi.html.
• For testing online via OnVue online proctored please make sure you review, and complete necessary sy stem checks by visiting: https://www.pearsonvue.com/us/en/pmi/onvue.html F or those requiring accommodations, the PMI-CPMAI offers accessibility options during the exam on Pearson VUE. F ull details can be found in the PMI Certification Handbook and within the examination scheduling instructions. RETAKING THE EXAM If you do not pass the exam on your first attempt, we encourage you to continue studying and review the PMI-CPMAI Exam Prep material and resources and then retake the exam. The suggested period for exam preparation prior to retaking the exam is 30 days. You may take the examination up to a total of three times within a 1-year (365 days) eligibility period. This policy is designed to uphold exam security and reduce the overexposure of examination questions to individual candidates. However, during this year you are welcome to apply for any other PMI certification. Each subsequent exam attempt will require submitting an exam fee prior to scheduling the exam. The fees for obtaining the PMI-CPMAI certification are subject to regional and membership pricing rules. However, membership is NOT required to obtain the PMI-CPMAI. Initial course and examination fees must be paid before you can take the course and scheduling your exam will be available upon the completion of the PMI-CPMAI Exam Prep Course . Onc e an examination date is confirmed and scheduled, you may be subject to cancellation or no-show fees. M aintaining your PMI-CPMAI certification will require a payment based on regional and membership pricing rules. We currently support USD, Euros, BRL, and INR currencies. PMI accepts credit cards and wire transfers as valid forms of payment methods. Klarna and Afterpay, third-party “buy now, pay later” services, are also availabl e for orders of $50 and over. I f PMI membership is obtained after you submit payment for the certification, PMI will not refund the difference. Review all the benefits of PMI membership . F or more information about certification fees, please see the PMI Certification Handbook. CONTINUING CERTIFICATION REQUIREMENTS (CCR) PROGRAM . O nce you have successfully earned your PMI-CPMAI certification, maintain your certification by completing 30 professional development units (PDUs) every 3 years. For details on the CCR Program and instructions on how to earn and track PDUs in CCR, please review the Continuing Certification Requirements (CCR) Handbook by visiting https://www.pmi.org/certifications/certification-resources/maintain Exam candidates should be aware that the PMI-CPMAITM examination is not written according to any single text or singularly supported by any particular reference. PMI does not endorse specific review courses resources, references, or other materials for certification preparation. The references listed below are not inclusive of all resources that may be utilized and should not be interpreted as a guaranteed means of passing the exam. P roject Management Institute (PMI). (2025). AI in Project Management. Retrieved from https://www.pmi.org/learning/ai-in-project-management Project Management Institute (PMI). (2025). Free Introduction: PMI Certified Professional in Managing AI (PMI-CPMAI) TM Retrieved from www.pmi.org/dcpdp/sku/EL185 Project Management Institute (PMI). (2025). AI Today Podcast. Retrieved from https://www.pmi.org/ai-today-podcast Project Management Institute (PMI). (2024). The Seven Patterns of AI. Retrieved from https://www.pmi.org/blog/seven-patterns-of-ai Project Management Institute (PMI). (2025). Top 10 Ethical Considerations for AI Projects. Retrieved from https://www.pmi.org/blog/top-10-ethical-considerations-for-ai-projects Project Management Institute (PMI). (2025). AI Data Governance Best Practices. Retrieved from https://www.pmi.org/blog/ai-data-governance-best-practices Project Management Institute (PMI). (2024). Preparing Project Managers for an AI-Driven Future. Retrieved from https://www.pmi.org/blog/preparing-project-managers-for-an-ai-driven-future Project Management Institute (PMI). (2025). Leading and Managing AI Projects Digital Guide . Retrieved from https://www.pmi.org/standards The development o f the PMI-CPMAI c ertification program and Exam C ontent Outline would n ot have been possible without the significant contributions of the Certification Steering Committee and DACUM Workshop Attendees. A special thanks to: Dr. Wanda Curlee , PMP, CPMAI, Glob al Exam Content Producer George Fountain Jr. MBA, PMP, CPMAI, Program Manager Christina Kucek, PMP, CPMAI, CSM, Senior Product Ma nager Jean-Philippe Martin, CPMAI, Security Consultant Chris Mielke, PMP, CPMAI, CSM Head of Project Management Nirvanna Rampersad, PMP, CPMAI, Technical Director Jacqueline Roemmele, PMP, CPMAI Senior Project Manager Johan Sporre, PMP, CPMAI, Engineering Manager Esteban Villegas, PMP, PgMP, PMI-ACP, CPMAI, GMP-b, P rogram M ana ger Kathleen Walch, CPMAI, Director of AI Community and Engagement.

Tip: In scenario questions, force this sequence: (1) objective, (2) constraint/risk, (3) what evidence is missing, (4) safest next step that still moves delivery forward.