PMI-CPMAI Exam Syllabus

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

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

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

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

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

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

https://www.pmi.org/certifications/ai-project-management-cpmai