C1000-195
Coming Soon
Expected availability announced soon
This course is in active development. Preview the scope below and create a free account to be notified the moment it goes live.
C1000 195 Watsonx Governance
The course teaches IBM Certified watsonx Governance Lifecycle Advisor v1 – Associate (C1000-195) fundamentals, covering AI lifecycle management, model monitoring, compliance, validation, and approval workflows, enabling practitioners to implement governance best practices.
90
Minutes
62
Questions
60/100
Passing Score
$200
Exam Cost
Who Should Take This
Data scientists, AI engineers, and governance analysts who manage or oversee AI models in enterprise environments are ideal candidates. They possess at least two years of experience with model development or deployment and seek to validate compliance, monitor performance, and streamline approval processes using IBM watsonx Governance.
What's Covered
1
Domain 1: AI Lifecycle Management Fundamentals
2
Domain 2: Model Monitoring and Performance Management
3
Domain 3: Regulatory Compliance and Standards
4
Domain 4: Model Validation and Testing
5
Domain 5: Approval Workflows and Policy Management
6
Domain 6: Documentation and Audit Management
What's Included in AccelaStudy® AI
Adaptive Knowledge Graph
Practice Questions
Lesson Modules
Console Simulator Labs
Exam Tips & Strategy
20 Activity Formats
Course Outline
75 learning goals
1
Domain 1: AI Lifecycle Management Fundamentals
3 topics
AI Governance Framework Basics
- Identify the core components of watsonx.governance platform including model inventory, risk assessment, and monitoring capabilities
- Define AI lifecycle stages from development through deployment and retirement within watsonx.governance framework
- Apply governance principles to establish AI model oversight processes using watsonx.governance tools
- Analyze the relationship between AI lifecycle phases and governance checkpoints in enterprise deployments
- Evaluate governance maturity levels and their impact on AI model lifecycle management effectiveness
Model Inventory and Catalog Management
- List essential metadata fields required for AI model registration in watsonx.governance model inventory
- Create model catalog entries with proper versioning, lineage tracking, and dependency mapping
- Configure automated model discovery and registration workflows within watsonx.governance platform
- Analyze model inventory data to identify gaps in documentation, ownership, or compliance status
- Assess model portfolio risk profiles based on inventory metadata and usage patterns
Initial Risk Assessment Processes
- Identify key risk factors for AI models including bias, fairness, explainability, and business impact
- Apply risk assessment templates and scoring frameworks within watsonx.governance platform
- Execute preliminary risk evaluations for new AI models entering the governance pipeline
- Analyze risk assessment results to determine appropriate governance controls and monitoring requirements
- Evaluate risk mitigation strategies and their effectiveness in reducing identified AI model vulnerabilities
2
Domain 2: Model Monitoring and Performance Management
3 topics
Drift Detection and Management
- Define data drift, concept drift, and model performance drift types in AI monitoring contexts
- Configure drift detection thresholds and alerting mechanisms using watsonx.governance monitoring tools
- Implement automated drift monitoring workflows for production AI models with custom metrics
- Analyze drift patterns to determine root causes and recommend corrective actions
- Evaluate the effectiveness of different drift detection algorithms for various model types and data domains
Bias and Fairness Monitoring
- Identify common bias types including statistical parity, equalized odds, and demographic parity in AI systems
- Apply fairness metrics and bias detection algorithms using watsonx.governance fairness monitoring capabilities
- Configure bias monitoring dashboards with protected attribute analysis and disparity measurements
- Analyze fairness test results to identify potential discrimination patterns across demographic groups
- Assess bias mitigation techniques including preprocessing, in-processing, and post-processing approaches
Explainability and Interpretability
- Describe explainability techniques including LIME, SHAP, and global surrogate models for AI transparency
- Generate model explanations and feature importance rankings using watsonx.governance explainability tools
- Implement explanation monitoring to track changes in model decision patterns over time
- Analyze explanation outputs to validate model behavior and identify potential issues or anomalies
- Evaluate trade-offs between model accuracy and explainability for different business use cases
3
Domain 3: Regulatory Compliance and Standards
3 topics
EU AI Act Compliance
- Identify EU AI Act risk categories including unacceptable risk, high-risk, limited risk, and minimal risk classifications
- Apply EU AI Act requirements to classify AI systems and determine compliance obligations
- Configure compliance monitoring workflows for high-risk AI systems using watsonx.governance compliance features
- Analyze AI system characteristics against EU AI Act prohibited practices and restrictions
- Evaluate conformity assessment procedures and CE marking requirements for AI systems in EU markets
NIST AI Risk Management Framework
- List NIST AI RMF core functions including govern, map, measure, and manage for AI risk management
- Implement NIST AI RMF controls and subcategories using watsonx.governance policy management tools
- Map organizational AI practices to NIST AI RMF categories and create compliance assessment reports
- Analyze gaps between current AI governance practices and NIST AI RMF recommendations
- Assess NIST AI RMF implementation maturity and develop improvement roadmaps for AI governance programs
Industry Standards and Best Practices
- Identify ISO/IEC 23053, ISO/IEC 23894, and other emerging AI governance standards and their requirements
- Apply industry best practices for AI ethics, transparency, and accountability in governance frameworks
- Configure audit trails and documentation workflows to meet multiple regulatory and industry standards
- Compare different regulatory approaches and their impact on AI governance strategy decisions
- Evaluate regulatory landscape evolution and its implications for long-term AI governance planning
4
Domain 4: Model Validation and Testing
2 topics
Validation Methodologies
- Define model validation techniques including cross-validation, holdout testing, and temporal validation approaches
- Execute comprehensive model validation workflows using watsonx.governance testing and validation tools
- Configure automated validation pipelines with performance benchmarks and acceptance criteria
- Analyze validation results to identify model weaknesses, overfitting, or generalization issues
- Evaluate validation methodology effectiveness and recommend improvements for different model types
Testing Frameworks and Quality Assurance
- Identify testing categories including unit testing, integration testing, and end-to-end testing for AI models
- Implement AI model testing frameworks with automated test case generation and execution
- Create comprehensive test suites covering edge cases, adversarial inputs, and boundary conditions
- Analyze test coverage metrics and identify gaps in AI model quality assurance processes
- Assess testing strategy adequacy for different AI model complexity levels and deployment environments
5
Domain 5: Approval Workflows and Policy Management
2 topics
Governance Policy Framework
- Describe governance policy components including rules, controls, exceptions, and enforcement mechanisms
- Create AI governance policies using watsonx.governance policy editor with conditional logic and triggers
- Configure policy enforcement points across AI lifecycle stages with automated compliance checking
- Analyze policy effectiveness through compliance metrics and violation pattern analysis
- Evaluate policy framework scalability and adaptability for evolving AI governance requirements
Approval Workflow Design
- Identify approval workflow elements including stakeholders, gates, escalation paths, and approval criteria
- Design multi-stage approval workflows for AI model deployment using watsonx.governance workflow builder
- Configure conditional approval routing based on risk assessment scores and compliance status
- Analyze workflow bottlenecks and approval cycle times to optimize governance efficiency
- Evaluate workflow automation opportunities while maintaining appropriate human oversight and control
6
Domain 6: Documentation and Audit Management
2 topics
Model Cards and Documentation
- List required elements for AI model cards including intended use, performance metrics, limitations, and bias considerations
- Generate comprehensive model cards using watsonx.governance documentation templates and automation tools
- Maintain version-controlled model documentation with automated updates from monitoring and testing systems
- Analyze documentation completeness and accuracy across model portfolio for compliance readiness
- Assess documentation quality standards and their effectiveness for stakeholder communication and regulatory compliance
Audit Trails and Reporting
- Identify audit trail requirements including user actions, system events, approval decisions, and data lineage
- Configure comprehensive audit logging across watsonx.governance platform with immutable record keeping
- Generate compliance reports and audit summaries for regulatory reviews and internal assessments
- Analyze audit data to identify patterns, anomalies, and potential governance control weaknesses
- Evaluate audit trail completeness and retention strategies for long-term compliance and forensic analysis
Scope
Included Topics
- All domains of C1000-195 IBM Certified watsonx Governance Lifecycle Advisor v1 - Associate: watsonx.governance: AI lifecycle management, model inventory, risk assessment; model monitoring (drift, bias, fairness, explainability); regulatory compliance (EU AI Act, NIST AI RMF); model validatio.
- Exam-specific technical content covering n, testing, approval workflows; governance policies, model cards, documentation, audit trails..
Not Covered
- Topics outside the C1000-195 exam scope and other certification levels.
- Current pricing, promotional offers, and vendor-specific values that change over time.
- Implementation details for competing vendor products and platforms.
Official Exam Page
Learn more at IBM
C1000-195 is coming soon
Adaptive learning that maps your knowledge and closes your gaps.
Create Free Account to Be Notified