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PMI CPMAI Exam Syllabus Topics:

Section	Weight	Objectives
Managing AI	8%	- Managing AI project teams and resources - Risk management in AI projects - Stakeholder management
AI Fundamentals	16%	- AI capabilities and limitations - Types of AI and Machine Learning - Concepts and terminology of Artificial Intelligence
Data for AI	13%	- Data preparation and preprocessing - Data strategy and governance - DataOps concepts
CPMAI Methodology	41%	- Phase I: Problem Identification 1. Evaluate initial AI feasibility 2. Identify business needs and solutions - Phase III: Algorithm Selection & Model Training 1. Manage model training and configuration 2. Oversee AI/ML model technique selection - Phase VI: Iteration & Monitoring 1. Continuous monitoring and iteration 2. Retraining and model drift management - Phase II: Data Identification & Curation 1. Assess data readiness and quality 2. Identify data needs - Phase V: Operationalization 1. Plan for deployment and integration 2. Define success criteria and monitoring - Phase IV: Model Evaluation 1. Oversee AI/ML model QA/QC 2. Evaluate model performance against metrics
Trustworthy AI	9%	- Transparency and explainability - Privacy and security - Ethical considerations and bias
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 (Q84-Q89):

NEW QUESTION # 84
A healthcare organization plans to implement an AI system to assist with patient diagnosis. The project manager needs to conduct an ethical risk assessment to ensure the AI system does not propagate bias. What is an effective method that meets the project manager's objective?

A. Implementing fairness-aware algorithms
B. Using interpretable models for decision-making
C. Ensuring diverse training datasets
D. Conducting bias audits during development

Answer: D

Explanation:
Conducting bias audits during development directly supports an ethical risk assessment by systematically testing whether the AI system produces biased outcomes before it is deployed.
This helps identify, measure, and address bias risks early in the diagnostic workflow.

NEW QUESTION # 85
You're testing your model and it is overly sensitive to the fluctuations of data and having trouble generalizing. What type of problem is this?

A. You are overfitting the data
B. You have selected the wrong data
C. You are underfitting the data
D. You have selected the wrong algorithm

Answer: A

NEW QUESTION # 86
A healthcare provider plans to deploy an AI system to predict patient readmissions. The project manager needs to conduct a risk assessment to ensure patient safety and data integrity. What is an effective method to help ensure the AI system adheres to ethical standards?

A. Conducting a stakeholder impact analysis
B. Implementing a data encryption protocol
C. Using an explainability framework
D. Performing continuous monitoring and auditing

Answer: D

Explanation:
PMI guidance for responsible and trustworthy AI stresses that ethical performance is not a one- time checkbox; it requires ongoing oversight, including transparency, accountability, and continuous controls. PMI-CPMAI's exam outline explicitly highlights maintaining audit trails for algorithmic decision-making, implementing compliance monitoring mechanisms, and managing accountability documentation--foundational practices that align directly with continuous monitoring and auditing. In high-stakes healthcare use cases like readmission prediction, model drift, data drift, and shifting patient populations can degrade performance and fairness over time, which can create patient safety risks. Continuous monitoring enables the team to detect deteriorating accuracy, emerging bias, and unexpected failure modes early; auditing supports traceability of decisions, data lineage, and adherence to governance requirements. PMI also emphasizes that ethical AI demands validation and transparency, noting that accountability and continuous monitoring are crucial to maintain ethical standards and minimize undesirable outcomes.

NEW QUESTION # 87
An AI project team is assessing the scalability of a healthcare solution. Which factor should the project manager consider to help ensure the solution is scalable?

A. Integration with the existing infrastructure
B. Compliance with data regulations
C. Ability to handle increased loads
D. Human oversight requirements

Answer: C

Explanation:
The ability to handle increased loads is central to scalability because the healthcare AI solution must continue performing reliably as data volume, users, requests, and operational demand grow.

NEW QUESTION # 88
Your team is working on a new loan decision model that takes a number of factors and data points into consideration and then automatically approves or denies a loan. After a month in operation someone does a review and notices that the system is denying a large number of loans from a certain demographic when all other factors from people in other regions (such as age, salary, and credit score) are the same. What is most likely happening here?

A. Data privacy issues leading to data sharing concerns
B. Generative AI models hallucinating data results
C. Nothing is wrong, algorithmic decisions will never be 100%
D. Biased data sets leading to algorithmic discrimination

Answer: D

Explanation:
Biased datasets can cause the model to discriminate against certain demographics, resulting in unfair or unequal loan denial decisions despite similar attributes.

NEW QUESTION # 89
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