22 Aug - 23 Aug
6:30 am – 1:30 pm (EDT)
7 hrs - Sat, Sun: 2 sessions
Virtual
$
Candidates can attempt the exam up to 3 times within a 1-year (365-day) eligibility period.
PMI recommends waiting 30 days before retaking the exam to allow adequate preparation.
A retake fee is required for each additional exam attempt.
Candidates are encouraged to review the PMI-CPMAI Exam Prep Course before reattempting.
You may apply for other PMI certifications during your eligibility period.
Exam Modes: Available at a Pearson VUE test center (recommended) or online with remote proctoring (OnVUE).
Exam Provider: Conducted through Pearson VUE.
Online Exam: Requires a system compatibility check and an online check-in process before the exam.
Certification Validity: You have 12 months from the date of purchase to complete the certification.
Accessibility Support: Exam accommodations are available for eligible candidates through Pearson VUE.
Scheduling: Follow the exam scheduling instructions provided by PMI after completing the Exam Prep Course.
Ensure AI privacy, security, and regulatory compliance.
Promote transparency, explainability, and responsible AI practices.
Detect and mitigate bias in AI models and data.
Implement AI governance, accountability, and audit trails.
Support ethical and trustworthy AI throughout the project lifecycle.
Identify business problems suitable for AI solutions.
Assess AI feasibility, risks, and organizational readiness.
Define project scope, success criteria, and expected ROI.
Develop business cases and AI solution strategies.
Plan resources and drive successful AI adoption.
Define data requirements for AI initiatives.
Identify, collect, and validate relevant data sources.
Ensure data quality, privacy, security, and compliance.
Assess data readiness for AI model development.
Communicate data insights and recommendations to stakeholders.
Select appropriate AI/ML models and algorithms.
Oversee data preparation, model training, and testing.
Ensure model quality, performance, and validation.
Evaluate model readiness for production deployment.
Support data-driven go/no-go decisions.
Plan and manage AI solution deployment.
Monitor model performance, KPIs, and governance.
Manage AI lifecycle, maintenance, and continuous improvement.
Develop transition, support, and contingency plans.
Capture lessons learned and ensure long-term business value.
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