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PMI CPMAI Exam Syllabus Topics:

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

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PMI Cognitive Project Management in AI (PMI-CPMAI) Sample Questions (Q19-Q24):

NEW QUESTION # 19
During the initial phase of an AI project, the team is assessing project success criteria. The project manager discovers that the project may be violating some compliance rules.
What problem describes the issue the project team is facing?

A. Lack of clarity on the project's business objective
B. Absence of a clear AI go/no-go assessment
C. Inadequate separation of cognitive and noncognitive software
D. Failure to identify applicable data regulations early on

Answer: D

NEW QUESTION # 20
Data Engineering is 80%+ of most AI projects, so building a good Data Engineering Environment is key to AI Project Success. As the manager of this project, you need to make sure you have correct staffing needs.
What's the most critical role to staff for in the Big Data / Data Engineering Environment?

A. Data Scientists
B. Data Engineering and Data Scientists
C. Data Engineering
D. All roles are critical to staff in the Four different AI Tech environments
E. Senior management

Answer: C

NEW QUESTION # 21
In what way would you be using Generative AI if you used the results of the Generative AI solution to improve and accelerate your job?

A. As a programmatic approach for automation
B. Used for Hyperpersonalization
C. As an Augmented Intelligence system
D. As an autonomous system removing the human from the loop

Answer: C

Explanation:
Using Generative AI results to enhance and accelerate your work represents Augmented Intelligence, where AI assists and amplifies human capabilities without fully replacing human decision-making.

NEW QUESTION # 22
A project manager is reviewing the performance of an AI model used for predictive analytics in sales. The model's accuracy is within acceptable limits; however, its precision is low.
What is the cause for the precision issue?

A. The training data is unbalanced
B. The model is overfitting the training data
C. The feature selection process is flawed
D. The model is underfitting the validation data

Answer: A

NEW QUESTION # 23
A logistics company is planning to separate cognitive and noncognitive parts of an AI project.
They need to manage teams working on these parts. What is a necessary initial task?

A. Conducting a skills assessment to assign team members accordingly
B. Implementing a customer-driven feedback loop in planning
C. Utilizing a project management software to monitor progress
D. Developing a project charter outlining responsibilities for each team

Answer: A

Explanation:
Conducting a skills assessment helps determine which team members are best suited for cognitive AI work, such as model development and data science, and which are better suited for noncognitive automation or traditional implementation tasks. This supports proper team assignment before execution begins.

NEW QUESTION # 24
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