Free Practice Questions for PMI Certified Professional in Managing AI Certification

Oversee privacy and security plan

• data governance protocols
• personally identifiable information (PII)
• encryption
• access controls
• privacy impact assessments
• GDPR
• CCPA
• secure data handling

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

• model selection criteria
• decision rationale
• transparent reporting
• data sources
• preprocessing steps
• explainability requirements
• audit trails
• algorithmic decision-making
• model interpretability tools

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

• demographic imbalances
• representation imbalances
• fairness testing
• bias detection metrics
• monitoring systems
• discriminatory patterns
• bias mitigation techniques

Monitor regulatory and policy compliance

• evolving AI regulations
• industry standards
• sector-specific compliance
• AI governance
• compliance monitoring
• reporting mechanisms
• regulatory audits

Manage accountability documentation and audit trail

• AI model development decisions
• version control
• stakeholder approvals
• go/no-go decision points
• chain of custody
• training data
• test data
• accountability reports

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

• stakeholder interviews
• business pain points
• automation opportunities
• user personas
• use cases
• AI patterns
• problem statements

Evaluate initial AI feasibility

• technical viability
• data availability
• data quality
• computational resource requirements
• organizational readiness
• traditional solution alternatives

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

• failure modes
• safety implications
• cybersecurity vulnerabilities
• ethical implications
• reputational risks
• business continuity risks
• risk mitigation strategies
• contingency plans

Develop AI project scope statement

• project boundaries
• deliverables
• success criteria
• performance metrics
• in-scope functionality
• out-of-scope functionality
• assumptions
• constraints
• business objectives

Determine ROI

• expected benefits
• total cost of ownership
• infrastructure
• maintenance
• business case
• financial justification
• ROI metrics
• cost-benefit analysis

Manage adoption/integration risks

• organizational change management
• user resistance
• adoption barriers
• system integration
• workflows
• training strategies
• communication strategies
• adoption metrics

Draft AI solution

• high-level architecture
• data flow
• processing requirements
• AI model types
• algorithmic approaches
• integration points
• deployment considerations
• operational considerations

Define success criteria (e.g., KPIs, metrics)

• performance indicators
• business impact metrics
• success thresholds
• technical performance benchmarks
• user satisfaction
• adoption measurement criteria

Support business case creation

• financial data
• projected benefits
• cost estimates
• executive presentations
• technical expertise
• business case validation

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

• skill requirements
• team composition
• hardware needs
• infrastructure needs
• external contractors
• resource allocation
• timeline
• specialized AI tools
• platforms

Define required data

• data types
• data formats
• data volume
• sampling strategies
• temporal requirements
• granularity requirements
• data quality standards
• acceptance criteria
• business objectives

Identify data SMEs

• domain experts
• data sources
• business users
• data context
• data meaning
• data stewards
• data governance teams
• technical experts
• data systems
• communication channels

Identify data sources and locations

• internal databases
• data warehouses
• external data sources
• third-party data providers
• cloud storage
• distributed data repositories
• legacy systems
• historical data archives
• data ownership
• access permissions

Coordinate AI workspace and infrastructure

• computing resources
• data processing
• model training
• secure development environments
• data storage
• backup systems
• collaboration tools
• version control systems
• security compliance
• governance requirements

Gather required data

• data extraction
• data transfers
• data migrations
• data collection processes
• data completeness
• data accuracy
• data refresh
• update procedures

Check data privacy, compliance, and access

• data usage rights
• licensing agreements
• data protection regulations
• access controls
• user permissions
• privacy impact assessments
• data lineage
• audit purposes

Oversee data evaluation

• data quality dimensions
• accuracy
• completeness
• consistency
• data distributions
• biases
• data freshness
• data relevance
• data schema
• data structure
• exploratory data analysis

Determine if data meets solution needs

• data requirements
• data specifications
• data sufficiency
• robust AI models
• data gaps
• data representativeness
• go/no-go decisions
• data readiness assessment

Convey data understanding to leadership

• executive summaries
• data assessment findings
• visualizations
• reports
• data insights
• data readiness status
• recommendations
• technical data concepts
• business-relevant language
• data preparation progress

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

• algorithms
• supervised learning
• unsupervised learning
• reinforcement learning
• model complexity
• performance
• interpretability
• model architecture
• algorithm selection criteria

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

• model testing protocols
• quality assurance procedures
• configuration management
• model versions
• parameters
• model performance metrics
• peer reviews
• technical validation
• coding standards

Manage AI/ML model training

• training schedules
• resource allocation
• training progress
• computational resource utilization
• hyperparameter tuning
• optimization activities
• cross-validation
• model selection processes
• training data versioning
• experiment tracking

Manage data transformation to conduct data preparation

• data cleaning
• preprocessing workflows
• feature engineering
• feature selection
• data normalization
• data standardization
• data augmentation
• synthetic data generation
• data transformation reproducibility

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

• data quality assessments
• model training
• data preprocessing
• transformation results
• data representativeness
• potential bias issues
• data readiness
• data quality findings

Verify model ready for operationalization go/no-go decision

• model performance
• success criteria
• model robustness
• generalization capabilities
• deployment readiness
• infrastructure requirements
• model documentation
• operational procedures
• model deployment approval

Manage creation of AI solution deployment plan

• deployment strategy
• timeline
• infrastructure requirements
• resource allocation
• system integration
• rollback procedures
• contingency plans
• deployment checklists
• validation criteria

Manage AI solution deployment

• deployment activities
• implementation issues
• system functionality
• performance
• production environment
• user access provisioning
• security configurations
• post-deployment verification
• testing

Oversee model governance

• model lifecycle management
• model versioning
• change control processes
• model performance
• drift detection
• model updates
• retraining schedules
• governance policies
• standards

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

• monitoring dashboards
• business metrics
• technical metrics
• key performance indicators
• success measures
• model performance trends
• degradation patterns
• performance reports
• alerting systems
• performance threshold breaches

Prepare final report/lessons learned

• project outcomes
• objectives
• lessons learned
• best practices
• areas for improvement
• knowledge transfer documentation
• project results

Manage AI solution transition plan

• operational support
• knowledge transfer
• production support teams
• ongoing maintenance
• support procedures
• roles and responsibilities
• handover documentation
• training materials

Oversee AI solution contingency plan

• incident response procedures
• AI system failures
• backup strategies
• disaster recovery strategies
• escalation procedures
• business continuity plans
• AI service disruptions
• contingency procedures testing