PMI-CPMAI | Certified Associate in Project Management Exam Prep Course
 
The 21-hour PMI-CPMAI Exam Prep Course provides the knowledge and skills to pass the exam and manage AI projects effectively.
Organized around the six CPMAI methodology phases, it uses scenario-based exercises, case studies and a downloadable workbook to help you apply concepts immediately. The self-paced format includes multimedia content, a guided review of Exam Content Outline (ECO) references, and independent study activities—so you can learn at your own pace while building a strong understanding of the material.
 
With PMI-CPMAI, you’ll learn how to:
Turn bold AI visions into clear, achievable project plans.
Navigate fast-changing technologies without tool-specific training.
Unite cross-functional teams around a shared process.
Deliver outcomes that are ethical, measurable, and built to withstand business scrutiny.
No matter your role (Project Manager, technologist, data expert, or consultant) PMI-CPMAI helps you grow your skills and your career in a market that rewards AI-savvy leaders.
 
Target Audience
It is particularly suited for:
Project, program, and product managers leading AI-driven transformations.
Data professionals seeking structured AI project methodologies.
Technologists, consultants, and IT professionals managing AI integration.
Directors & senior managers steering AI innovation.
 
Eligibility Requirements
To earn the PMI-CPMAI certification, you must:
Be at least 18 years old.
Complete the PMI-CPMAI exam prep course (included in this bundle) to understand the CPMAI methodology.
 
Course Outline
The PMI-CPMAI course ensures you have access to the most current practices and strategies for managing AI projects by covering the following topics:
The Need for AI Project Management: Discover why AI projects struggle, how iterative delivery supports success, and how CPMAI ensures ethical, effective outcomes.
Matching AI with Business Needs (Phase I): Align AI solutions to real business needs, assess feasibility, define ROI, and set clear project scope.
Identifying Data Needs for AI Projects (Phase II): Select the right data, ensure compliance, and build the infrastructure to support AI.
Managing Data Preparation Needs for AI Projects (Phase III): Transform raw data into AI-ready inputs through quality checks, augmentation, and compliance controls.
Iterating Development and Delivery of AI Projects (Phase IV): Build and validate models, from machine learning to generative AI.
Testing & Evaluating AI Systems (Phase V): Test and monitor AI models, address drift, and ensure results are reliable, explainable, and aligned with goals.
Operationalizing AI (Phase VI): Operationalize AI responsibly, manage governance, and plan for continuous improvement.
 
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Debido a las constantes actualizaciones de los contenidos de los cursos por parte del fabricante, el contenido de este temario puede variar con respecto al publicado en el sitio oficial, sin embargo, Netec siempre entregará la versión actualizada de éste.