Cognitive Project Management in AI (CPMAI)™ v7

This workshop is ideal for professionals who are involved in leading, managing, or supporting AI/ML initiatives, including:

• Project and Program Managers
• AI/ML Leads and Data Science Managers
• Product Owners and Product Managers
• Digital Transformation Leaders
• Directors and Senior Managers leading AI initiatives
• IT and Enterprise Architects
• Innovation and Strategy Executives
• Business Analysts and Change Managers
• Risk, Compliance, and Governance Officers.

• AI Fundamentals
  • Understanding of AI Fundamentals and Evolution
  • Evaluating AI Applications and Patterns
  • Applying Machine Learning Fundamentals
• CPMAI Methodology
  • Differentiating AI Project Management Approaches
  • Executing the Business Understanding Phase
  • Managing the Data Understanding Phase
  • Coordinating the Data Preparation Activities
  • Determining the Approaches for Model Development
  • Conducting Model Evaluation and Maintenance
• Machine Learning
  • Applying Classification and Clustering Algorithms
  • Implementing Neural Networks and Deep Learning
  • Leveraging Generative AI and Large Language Models (LLMs)
  • Selecting Machine Learning Tools and Platforms
• Data for AI
  • Managing Data Fundamentals and Big Data Concepts
  • Implementing Data Governance and Management
  • Engineering Data Pipelines for AI
  • Executing Data Preparation and Transformation
• Managing AI
  • Evaluating Model Performance and Accuracy
  • Deploying Models for Production Environments
• Trustworthy AI
  • Establishing Ethical, Responsible, and Trustworthy AI Foundations
  • Implementing AI Privacy and Security
  • Ensuring AI Transparency and Explainability
  • Navigating AI Regulations and Frameworks

Upon completing this workshop, participants will be able to:

• Understand foundational AI concepts including machine learning, cognitive computing, and generative AI, and distinguish between different types of AI such as AGI, strong AI, and narrow AI.
• Evaluate and select appropriate AI use cases using the seven patterns of AI, recognizing both potential and limitations across various business contexts.
• Apply the CPMAI methodology across its six defined phases to manage AI projects in a structured, iterative, and outcomes-driven manner.
• Formulate AI-specific business questions and scope initiatives to align with strategic goals using tools like the DIKUW Pyramid and Go/No-Go assessments.
• Manage the AI data lifecycle, including data preparation, labeling, engineering, governance, and pipeline design tailored for AI workloads.
• Develop and evaluate AI models, including the integration of machine learning algorithms, deep learning architectures, and pre-trained LLMs in enterprise environments.
• Deploy and operationalize AI models responsibly in production settings while addressing drift, scalability, versioning, and lifecycle management.
• Ensure AI projects adhere to ethical, transparent, and regulatory standards, incorporating responsible AI frameworks and privacy protections.
• Communicate AI project performance and explainability effectively to stakeholders using both business KPIs and technical metrics.
• Avoid common pitfalls in AI initiatives, including managing stakeholder expectations, vendor hype, and challenges unique to AI beyond traditional IT or software projects.

The CPMAI v7 certification examination is comprised of 100 total questions. Of the 100 questions, 10 are considered pre-test questions. Pre-test questions do not affect the score and are used in examinations as an effective and legitimate way to test the validity of future examination questions. All questions are placed throughout the examination randomly.

• No of scored questions: 90
• No. of Pretest (Unscored) Questions: 10
• Total Examination Questions: 100
• Alloted Exam Time: 2 hours (120 minutes).

For the CPMAI exam, there is no scheduled break. The examination is preceded by a tutorial and followed by a survey, both of which can take up to 15 minutes to complete. The time used to complete the tutorial and survey is not counted against total testing time.