Artificial Intelligence Governance Professional
Foundations of AI governance, laws such as the EU AI Act, and the AI lifecycle.
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Foundations of AI governance, laws such as the EU AI Act, and the AI lifecycle.
Prepare for Anthropic's CCAO-F exam with scenario-based questions on using Claude at work: writing and refining prompts, checking outputs for accuracy, choosing features and models, setting up Projects and connectors, and using AI responsibly. Every question carries a worked explanation.
Agentic architecture and orchestration, tool design and MCP, and Claude Code workflows.
Prepare for Anthropic's CCAR-P exam with scenario-based questions on owning a Claude system in production: integration and RAG design, evaluation and optimisation, governance and compliance, and communicating architectural decisions to stakeholders. Every question carries a worked explanation.
Privacy program frameworks and governance, assessing and protecting data, and incidents.
Securing AI in the enterprise: governance and program management, AI risk and opportunity, and AI controls.
Cloud concepts, core services, security and compliance, and billing and support options.
AI and machine learning fundamentals, generative AI, foundation models, and responsible AI.
Agentic architecture and orchestration, tool design and MCP, and Claude Code workflows.
Prepare for Anthropic's CCAO-F exam with scenario-based questions on using Claude at work: writing and refining prompts, checking outputs for accuracy, choosing features and models, setting up Projects and connectors, and using AI responsibly. Every question carries a worked explanation.
Prepare for Anthropic's CCDV-F exam with scenario-based questions on building with Claude: the API and SDKs, agent construction and tool use, MCP servers, Claude Code configuration, and structured output. Every question carries a worked explanation.
Cloud concepts, Azure architecture and core services, identity, governance and cost.
Project management fundamentals, predictive methods, agile frameworks, and business analysis.
Security principles, governance, identity and access, network and cloud security, and security operations.
Security, compliance and identity concepts, Zero Trust, Entra, Defender and Purview.
Data governance, quality, metadata and modelling, master data, warehousing and BI, architecture and security.
Digital and data transformation, AI with Vertex AI, modernization, trust and security.
Security concepts, access controls, risk and monitoring, incident response, cryptography, and network, system and application security.
Designing secure, resilient, high-performing and cost-optimized cloud architectures.
Entra ID identities and governance, storage, compute, virtual networking, and backup.
Core machine learning, transformers and LLMs, prompt engineering and RAG, and trustworthy AI.
Security concepts, threats and mitigations, security architecture, security operations, and program management and oversight.
Essential AI knowledge, GPU infrastructure with CUDA and InfiniBand, and AI operations.
Monitoring and remediation, reliability, deployment automation, networking, and compliance.
Development with Lambda, DynamoDB and API Gateway, security with Cognito and IAM, and CI/CD.
Preparing and transforming data, modeling with DAX, visuals, and row-level security.
Data ingestion and transformation, data store management, operations, and data governance.
Planning projects, deploying to Compute Engine, GKE and Cloud Run, and IAM access.
Entra ID identities, Conditional Access, workload identities, and governance with PIM.
Purview information protection, data loss prevention and retention, and insider risk.
Microsoft Sentinel and Defender XDR, incident response, and threat hunting with KQL.
Foundations of AI governance, laws such as the EU AI Act, and the AI lifecycle.
Prepare for Anthropic's CCAR-P exam with scenario-based questions on owning a Claude system in production: integration and RAG design, evaluation and optimisation, governance and compliance, and communicating architectural decisions to stakeholders. Every question carries a worked explanation.
Privacy program frameworks and governance, assessing and protecting data, and incidents.
Leading people, running the project process with predictive, agile and hybrid approaches, and the business environment.
European data protection law and the GDPR, data subject rights, transfers and DPIAs.
Administering GPU clusters with Base Command Manager and Slurm, and DCGM troubleshooting.
Organizational complexity with Organizations and SCPs, hybrid networking, and migration.
The US privacy environment, sector laws such as HIPAA and CCPA, and government access.
Foundation model integration, data compliance, AI safety and governance, and testing GenAI apps.
IT risk governance, risk assessment, risk response and reporting, and technology and security controls.
Information security governance, security risk management, running a security program, and incident management.
The IS audit process, IT governance, systems development, operations and resilience, and protecting information assets.
Privacy fundamentals plus Singapore, Hong Kong and India law, and cross-border transfers.
Privacy in data collection, software security, privacy engineering and privacy by design.
Security and risk management, asset security, architecture, networks, identity and access, testing, operations and software security.
Designing identity, governance, data storage, business continuity, and infrastructure.
Zero Trust strategy and GRC, security operations, identity, and infrastructure security.
Source control, build and release pipelines, dependency management, and instrumentation.
Auditing an information security management system against ISO/IEC 27001, from audit principles through stage 1, stage 2 and findings.
Information security risk management with ISO/IEC 27005: context, assessment, treatment and other methods.
Auditing an AI management system against ISO/IEC 42001, from audit principles through stage 1, stage 2 and certification.
Aligning programs to strategy, the program life cycle, benefits management, stakeholder engagement and governance.
SDLC automation, infrastructure as code, monitoring and logging, and incident response.
Cloud architecture, data security, platform and application security, cloud operations, and legal, risk and compliance.
Canadian privacy law including PIPEDA and CASL, data security, and breach notification.
Cloud-native apps on Cloud Run and GKE, deploying with Cloud Build, and Pub/Sub.
VPC networks, load balancing and Cloud DNS, hybrid connectivity, and network security.
Cloud IAM access, VPC Service Controls, data protection with KMS and DLP, and compliance.
Designing data processing systems, Pub/Sub and Dataflow, BigQuery, and batch workloads.
Highly available databases on Cloud SQL, Spanner and Bigtable, migration and failover.
AutoML and BigQuery ML, serving models on Vertex AI, pipelines, and monitoring for drift.
Log data management, threat hunting, detection engineering with YARA-L, and response.
Server bring-up, control plane setup with Base Command Manager and Slurm, and cluster tests.
Spectrum-X and InfiniBand fabrics, RoCE, troubleshooting, and AI data center design.
Implementing an ISMS with ISO/IEC 27001 and the 27002 controls: planning, risk, operation, monitoring and certification.
Incident management with ISO/IEC 27035: planning, detection, response and lessons learned.
Implementing an AI management system with ISO/IEC 42001: AI concepts, planning, controls, monitoring and certification.
Running a cybersecurity program: governance, risk, controls, training, continuity and measurement.
Implementing the NIS 2 Directive: scope, governance, risk measures, incident reporting and testing.
Aligning the portfolio to strategy, governance, portfolio performance and value, portfolio risk, and communications.
Securing AI in the enterprise: governance and program management, AI risk and opportunity, and AI controls.
Responsible AI, business and data needs, building and evaluating models, and putting AI into operation.
The agile mindset, servant leadership, product value and backlog, and delivering in short iterations.
Detection with GuardDuty, incident response, WAF and Shield, IAM, and data protection with KMS.
Managing AI risk: governance and framework integration, life cycle risk, and running an AI risk program.
Assessing business needs, planning and analysing requirements, traceability and monitoring, and evaluating the solution.
Quality dimensions and rules, assessment and profiling, data quality operations, and improvement tools.
Matching and survivorship, MDM architectures and data sharing, reference data, and stewardship.
Auditing AI: governance and risk, AI operations across the model life cycle, and AI audit tools and techniques.
Aligning the PMO to strategy, designing and running it, improving its value, and building its people.
Risk strategy and planning, identifying and analysing risks, planning responses, and monitoring and closing risks.
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PMI, CAPM, PfMP, PgMP, PMI-ACP, PMI-PBA, PMI-RMP and PMP are registered marks and PMI-CPMAI and PMI-PMOCP are marks of Project Management Institute, Inc.