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PMI CPMAI Exam Syllabus Topics:

Section	Weight	Objectives
Trustworthy AI	9%	- Transparency and explainability - Privacy and security - Ethical considerations and bias
Data for AI	13%	- Data strategy and governance - DataOps concepts - Data preparation and preprocessing
AI Fundamentals	16%	- Concepts and terminology of Artificial Intelligence - AI capabilities and limitations - Types of AI and Machine Learning
Managing AI	8%	- Risk management in AI projects - Stakeholder management - Managing AI project teams and resources
CPMAI Methodology	41%	- Phase III: Algorithm Selection & Model Training 1. Manage model training and configuration 2. Oversee AI/ML model technique selection - Phase I: Problem Identification 1. Evaluate initial AI feasibility 2. Identify business needs and solutions - Phase VI: Iteration & Monitoring 1. Continuous monitoring and iteration 2. Retraining and model drift management - Phase V: Operationalization 1. Plan for deployment and integration 2. Define success criteria and monitoring - Phase IV: Model Evaluation 1. Evaluate model performance against metrics 2. Oversee AI/ML model QA/QC - Phase II: Data Identification & Curation 1. Assess data readiness and quality 2. Identify data needs
Machine Learning	13%	- Algorithms and models (e.g., NLP, Computer Vision) - Supervised, Unsupervised, and Reinforcement Learning - Deep Learning and Neural Networks

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PMI Cognitive Project Management in AI (PMI-CPMAI) Sample Questions (Q12-Q17):

NEW QUESTION # 12
A government project plans to implement an AI-based fraud detection system and the project team needs to define the success criteria. They identified potential improvements in detection accuracy, reduction in investigation time, and cost savings as key performance indicators (KPIs).
However, they are unsure how to effectively quantify these KPIs. Which two approaches should be used? (Choose two.)

A. Implement a continuous performance monitoring system.
B. Use random benchmarks without industry comparison.
C. Set fixed performance targets based on theoretical models.
D. Rely on only qualitative feedback from stakeholders.
E. Establish a baseline using historical data comparisons.

Answer: A,E

Explanation:
Establishing a baseline using historical data comparisons provides a measurable starting point for quantifying improvements in fraud detection accuracy, investigation time, and cost savings.
Continuous performance monitoring then tracks whether the AI system is meeting those KPI targets over time after implementation.

NEW QUESTION # 13
You're running an image recognition project and realize that you do not have enough data of a certain type of vehicle. What is the best course of action to get the additional labeled data you need?

A. Perform Data anonymization
B. Purchase the data from a third party
C. Perform Data Sampling
D. Perform Data Transformation & Multiplication

Answer: B

Explanation:
Purchasing additional labeled data from a reputable third party is the most direct way to obtain the specific, high-quality data needed to improve model training when existing data is insufficient.

NEW QUESTION # 14
Your team is looking to develop an RPA bot to help with back-office processes such as data entry. What type of bot should your team be creating?

A. Unattended bot
B. RPA is not the right solution to this problem
C. Business Process Outsourcing
D. Attended bot

Answer: A

Explanation:
An unattended bot operates autonomously without human intervention, making it ideal for back- office tasks like data entry that are repetitive and do not require user interaction.

NEW QUESTION # 15
A fintech AI project uses third-party data sources for credit risk modeling. The project manager is concerned about compliance and accountability if the external data quality changes. Which control best supports responsible and trustworthy AI delivery?

A. Allow each team to apply its own data definitions
B. Establish data governance and supplier controls, including auditability and monitoring
C. Only document model performance once at launch
D. Remove all external data sources immediately

Answer: B

Explanation:
PMI's trustworthy AI framing highlights governance, transparency, and accountability as essential ingredients for systems people can interpret and monitor. When third-party data feeds can change, the PMI-aligned approach is to establish governance and supplier controls that define data quality expectations, lineage, permitted uses, privacy constraints, and monitoring/audit mechanisms. This supports accountability by making data dependencies explicit and enabling early detection when upstream changes degrade model behavior.

NEW QUESTION # 16
A project manager is preparing a contingency plan for an AI-enabled underwriting platform.
During outages, the business must still make time-sensitive decisions. What strategy best supports business continuity?

A. Only increase marketing to offset the outage
B. Stop all underwriting until the AI system returns
C. Implement a manual override process with defined escalation and decision rules
D. Keep the AI system running without monitoring to avoid interruptions

Answer: C

Explanation:
PMI-CPMAI highlights the need to manage AI operational risks through structured contingency planning and trustworthy AI governance. A business continuity-aligned contingency strategy is a manual override with clear escalation and decision rules so critical underwriting decisions can continue when the AI platform is unavailable. This is consistent with CPMAI expectations for operational readiness and accountability: define alternate operating modes, ensure decision traceability, and maintain service reliability despite disruptions. Stopping all underwriting (B) fails the "must still decide" requirement.

NEW QUESTION # 17
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