CPMAI Domains, Tasks & Enablers Video Study Guide

Master CPMAI domains and tasks to excel in your certification. Gain confidence and knowledge to pass with flying colors.

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Course curriculum

1. Establish data governance protocols for personally identifiable information (PII)
2. Implement encryption and access controls for AI training data
3. Conduct privacy impact assessments for AI model deployment
4. Ensure compliance with GDPR, CCPA, and other data protection regulations
5. Design secure data handling procedures throughout the AI lifecycle
1. Document model selection criteria and decision rationale
2. Create transparent reporting on data sources and preprocessing
3. Establish explainability requirements for stakeholder communication
4. Maintain audit trails for algorithmic decision-making processes
5. Implement model interpretability tools and techniques
1. Analyze training data for demographic and representation imbalances
2. Perform fairness testing across different population groups
3. Implement bias detection metrics and monitoring systems
4. Review model outputs for discriminatory patterns
5. Apply bias mitigation techniques during model development
1. Track evolving AI regulations and industry standards
2. Ensure adherence to sector-specific compliance requirements
3. Coordinate with legal and compliance teams on AI governance
4. Implement compliance monitoring and reporting mechanisms
5. Maintain documentation for regulatory audits and reviews
1. Create comprehensive records of AI model development decisions
2. Establish version control for models, data, and training processes
3. Document stakeholder approvals and go/no-go decision points
4. Maintain chain of custody records for training and test data
5. Prepare accountability reports for executive and regulatory review
1. Conduct stakeholder interviews to understand business pain points
2. Analyze existing processes to identify automation opportunities
3. Define target user personas and use cases for AI solutions
4. Map business problems to appropriate AI patterns and approaches
5. Validate problem statements with subject matter experts
1. Assess technical viability of proposed AI solutions
2. Analyze data availability and quality for model training
3. Evaluate computational resource requirements and constraints
4. Review organizational readiness for AI implementation
5. Compare AI approaches against traditional solution alternatives
1. Identify potential failure modes and safety implications
2. Assess cybersecurity vulnerabilities in AI systems
3. Evaluate ethical implications of AI decision-making
4. Analyze reputational and business continuity risks
5. Develop risk mitigation strategies and contingency plans
1. Define project boundaries and deliverables for AI initiatives
2. Establish success criteria and performance metrics
3. Identify in-scope and out-of-scope functionality
4. Document assumptions and constraints for AI implementation
5. Align scope with business objectives and resource availability
1. Calculate expected benefits from AI solution implementation
2. Estimate total cost of ownership including infrastructure and maintenance
3. Develop business case with financial justification
4. Establish metrics for measuring return on investment
5. Create cost-benefit analysis for stakeholder decision-making
1. Assess organizational change management requirements
2. Identify potential user resistance and adoption barriers
3. Plan integration with existing systems and workflows
4. Develop training and communication strategies for end users
5. Monitor adoption metrics and address implementation challenges
1. Create high-level architecture for AI system design
2. Define data flow and processing requirements
3. Specify AI model types and algorithmic approaches
4. Document integration points with existing systems
5. Outline deployment and operational considerations
1. Establish measurable performance indicators for AI models
2. Define business impact metrics and success thresholds
3. Create technical performance benchmarks and targets
4. Develop user satisfaction and adoption measurement criteria
5. Align success metrics with organizational objectives
1. Gather financial data and projected benefits for business case
2. Collaborate with finance teams on cost estimates and projections
3. Develop compelling narratives for executive presentations
4. Provide technical expertise for business case validation
5. Review and refine business case documentation
1. Assess skill requirements for AI project team composition
2. Evaluate hardware and infrastructure needs for development and deployment
3. Identify gaps requiring external contractors or consultants
4. Plan resource allocation and timeline for project phases
5. Coordinate with procurement for specialized AI tools and platforms
1. Specify data types and formats needed for AI model training
2. Determine data volume requirements and sampling strategies
3. Identify temporal and granularity requirements for data collection
4. Define data quality standards and acceptance criteria
5. Map data requirements to business objectives and use cases
1. Locate domain experts with knowledge of relevant data sources
2. Engage business users who understand data context and meaning
3. Connect with data stewards and data governance teams
4. Identify technical experts familiar with data systems and structures
5. Establish communication channels with identified subject matter experts
1. Map internal databases and data warehouses containing relevant information
2. Explore external data sources and third-party data providers
3. Assess cloud storage and distributed data repositories
4. Inventory legacy systems and historical data archives
5. Document data ownership and access permissions
1. Provision computing resources for data processing and model training
2. Establish secure development environments for AI teams
3. Configure data storage and backup systems for project needs
4. Set up collaboration tools and version control systems
5. Ensure compliance with security and governance requirements
1. Execute data extraction from identified sources and systems
2. Coordinate data transfers and migrations to AI development environments
3. Implement data collection processes for ongoing data feeds
4. Validate data completeness and accuracy during collection
5. Establish data refresh and update procedures
1. Verify data usage rights and licensing agreements
2. Ensure compliance with data protection regulations and policies
3. Implement access controls and user permissions for data resources
4. Conduct privacy impact assessments for data usage
5. Document data lineage and usage for audit purposes
1. Assess data quality dimensions including accuracy, completeness, and consistency
2. Analyze data distributions and identify potential biases or gaps
3. Evaluate data freshness and relevance for AI model training
4. Review data schema and structure for modeling compatibility
5. Conduct exploratory data analysis to understand data characteristics
1. Compare available data against defined requirements and specifications
2. Assess data sufficiency for training robust AI models
3. Identify data gaps and develop strategies for addressing deficiencies
4. Validate data representativeness for target use cases
5. Make go/no-go decisions based on data readiness assessment
1. Prepare executive summaries of data assessment findings
2. Create visualizations and reports to communicate data insights
3. Present data readiness status and recommendations to stakeholders
4. Translate technical data concepts into business-relevant language
5. Provide regular updates on data preparation progress and challenges
1. Research and evaluate appropriate algorithms for specific use cases
2. Guide selection between supervised, unsupervised, and reinforcement learning approaches
3. Assess trade-offs between model complexity, performance, and interpretability
4. Coordinate with data scientists on model architecture decisions
5. Review algorithm selection criteria and decision documentation
1. Establish model testing protocols and quality assurance procedures
2. Implement configuration management for model versions and parameters
3. Monitor model performance metrics during development and testing
4. Coordinate peer reviews and technical validation of model designs
5. Ensure adherence to coding standards and best practices
1. Plan training schedules and resource allocation for model development
2. Monitor training progress and computational resource utilization
3. Coordinate hyperparameter tuning and optimization activities
4. Oversee cross-validation and model selection processes
5. Manage training data versioning and experiment tracking
1. Oversee data cleaning and preprocessing workflows
2. Coordinate feature engineering and selection activities
3. Manage data normalization and standardization processes
4. Supervise data augmentation and synthetic data generation
5. Ensure data transformation reproducibility and documentation
1. Conduct final data quality assessments before model training
2. Validate data preprocessing and transformation results
3. Assess data representativeness and potential bias issues
4. Make decisions on data readiness for model development
5. Document data quality findings and recommendations
1. Evaluate model performance against established success criteria
2. Assess model robustness and generalization capabilities
3. Review deployment readiness including infrastructure requirements
4. Validate model documentation and operational procedures
5. Make final approval decisions for model deployment
1. Develop a comprehensive deployment strategy and timeline
2. Plan infrastructure requirements and resource allocation
3. Coordinate with IT teams on system integration and deployment
4. Establish rollback procedures and contingency plans
5. Create deployment checklists and validation criteria
1. Coordinate deployment activities across technical teams
2. Monitor deployment progress and resolve implementation issues
3. Validate system functionality and performance in production environment
4. Manage user access provisioning and security configurations
5. Conduct post-deployment verification and testing
1. Establish model lifecycle management procedures
2. Implement model versioning and change control processes
3. Monitor model performance and drift detection
4. Coordinate model updates and retraining schedules
5. Ensure compliance with governance policies and standards
1. Implement monitoring dashboards for business and technical metrics
2. Track key performance indicators and success measures
3. Analyze model performance trends and degradation patterns
4. Generate regular performance reports for stakeholders
5. Establish alerting systems for performance threshold breaches
1. Document project outcomes and achievement of objectives
2. Capture lessons learned and best practices for future projects
3. Analyze what worked well and areas for improvement
4. Create knowledge transfer documentation for operational teams
5. Present final project results to stakeholders and leadership
1. Plan transition from project team to operational support
2. Coordinate knowledge transfer to production support teams
3. Establish ongoing maintenance and support procedures
4. Define roles and responsibilities for operational phase
5. Create handover documentation and training materials
1. Develop incident response procedures for AI system failures
2. Plan backup and disaster recovery strategies
3. Establish escalation procedures for critical issues
4. Create business continuity plans for AI service disruptions
5. Test and validate contingency procedures regularly

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Domain I: Support Responsible and Trustworthy AI Efforts | Task 1: Oversee privacy and security plan

Domain I: Support Responsible and Trustworthy AI Efforts | Task 2: Manage AI/ML transparency

Domain I: Support Responsible and Trustworthy AI Efforts | Task 3: Conduct bias checks

Domain I: Support Responsible and Trustworthy AI Efforts | Task 4: Monitor regulatory and policy compliance

Domain I: Support Responsible and Trustworthy AI Efforts | Task 5: Manage accountability documentation and audit trail

Domain II: Identify Business Needs and Solutions | Task 1: Identify problem to be solved

Domain II: Identify Business Needs and Solutions | Task 2: Evaluate initial AI feasibility

Domain II: Identify Business Needs and Solutions | Task 3: Conduct risk assessment(s)

Domain II: Identify Business Needs and Solutions | Task 4: Develop AI project scope statement

Domain II: Identify Business Needs and Solutions | Task 5: Determine ROI

Domain II: Identify Business Needs and Solutions | Task 6: Manage adoption/integration risks

Domain II: Identify Business Needs and Solutions | Task 7: Draft AI solution

Domain II: Identify Business Needs and Solutions | Task 8: Define success criteria

Domain II: Identify Business Needs and Solutions | Task 9: Support business case creation

Domain II: Identify Business Needs and Solutions | Task 10: Identify project resources

Domain III: Identify Data Needs | Task 1: Define required data

Domain III: Identify Data Needs | Task 2: Identify data SMEs

Domain III: Identify Data Needs | Task 3: Identify data sources and locations

Domain III: Identify Data Needs | Task 4: Coordinate AI workspace and infrastructure

Domain III: Identify Data Needs | Task 5: Gather required data

Domain III: Identify Data Needs | Task 6: Check data privacy, compliance, and access

Domain III: Identify Data Needs | Task 7: Oversee data evaluation

Domain III: Identify Data Needs | Task 8: Determine if data meets solution needs

Domain III: Identify Data Needs | Task 9: Convey data understanding to leadership

Domain IV: Manage AI Model Development and Evaluation | Task 1: Oversee AI/ML model technique(s)

Domain IV: Manage AI Model Development and Evaluation | Task 2: Oversee AI/ML model QA/QC (e.g., configuration management, model performance)

Domain IV: Manage AI Model Development and Evaluation | Task 3: Manage AI/ML model training

Domain IV: Manage AI Model Development and Evaluation | Task 4: Manage data transformation to conduct data preparation

Domain IV: Manage AI Model Development and Evaluation | Task 5: Verify data quality for go/no-go decision to conduct data preparation

Domain IV: Manage AI Model Development and Evaluation | Task 6: Verify model ready for operationalization go/no-go decision

Domain V: Operationalize AI Solution | Task 1: Manage creation of AI solution deployment plan

Domain V: Operationalize AI Solution | Task 2: Manage AI solution deployment

Domain V: Operationalize AI Solution | Task 3: Oversee model governance

Domain V: Operationalize AI Solution | Task 4: Oversee AI solution metrics

Domain V: Operationalize AI Solution | Task 5: Prepare final report/lessons learned

Domain V: Operationalize AI Solution | Task 6: Manage AI solution transition plan

Domain V: Operationalize AI Solution | Task 7: Oversee AI solution contingency plan

About this course