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PMI CPMAI Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: CPMAI Methodology	41%	- Phase VI: Iteration & Monitoring 1. Retraining and model drift management 2. Continuous monitoring and iteration - Phase I: Problem Identification 1. Identify business needs and solutions 2. Evaluate initial AI feasibility - Phase II: Data Identification & Curation 1. Assess data readiness and quality 2. Identify data needs - Phase III: Algorithm Selection & Model Training 1. Oversee AI/ML model technique selection 2. Manage model training and configuration - Phase IV: Model Evaluation 1. Evaluate model performance against metrics 2. Oversee AI/ML model QA/QC - Phase V: Operationalization 1. Plan for deployment and integration 2. Define success criteria and monitoring
Topic 2: Trustworthy AI	9%	- Privacy and security - Ethical considerations and bias - Transparency and explainability
Topic 3: Data for AI	13%	- DataOps concepts - Data preparation and preprocessing - Data strategy and governance
Topic 4: Machine Learning	13%	- Deep Learning and Neural Networks - Algorithms and models (e.g., NLP, Computer Vision) - Supervised, Unsupervised, and Reinforcement Learning
Topic 5: AI Fundamentals	16%	- Concepts and terminology of Artificial Intelligence - AI capabilities and limitations - Types of AI and Machine Learning
Topic 6: Managing AI	8%	- Stakeholder management - Risk management in AI projects - Managing AI project teams and resources

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PMI Cognitive Project Management in AI (PMI-CPMAI) Sample Questions (Q322-Q327):

NEW QUESTION # 322
The team is evaluating where the sources of the data for training are. What phase of CPMAI are they in?

A. Phase V
B. Phase III
C. Phase II
D. Phase IV
E. Phase VI
F. Phase I

Answer: C

NEW QUESTION # 323
Clean, well-labeled, datasets used for machine learning are partitioned into three subsets:
Training sets, Validation sets, and Test sets. As your team is doing this, what's the best way to split up this data?

A. Split by patterned subsampling
B. Split by random subsampling
C. Split by alphabetical order
D. Use the same data for all sets

Answer: B

Explanation:
Random subsampling ensures that training, validation, and test sets are representative and unbiased samples of the overall data, which helps the model generalize well and provides reliable evaluation.

NEW QUESTION # 324
A national health insurance company is embarking on a complex AI project to assist in coordinating patient care across its multiple hospital network. The AI system will analyze large amounts of patient data to coordinate care, improve patient outcomes, and optimize resource allocation. Numerous healthcare providers' data needs to be integrated. The data includes patient information which is private. The project needs to comply with data privacy regulations in various countries. Which critical step should be performed to optimize representative training data?

A. Improve data understanding and preparation.
B. Enhance the key performance indicator (KPI) metrics.
C. Increase the data set size without considering diversity.
D. Implement comprehensive bias detection metrics.

Answer: A

Explanation:
Improving data understanding and preparation is critical for optimizing representative training data because the team must assess data sources, quality, completeness, diversity, privacy constraints, and integration issues before training the AI system. This helps ensure patient data from multiple providers is usable, compliant, and representative across the hospital network.

NEW QUESTION # 325
An organization is establishing oversight for multiple AI initiatives and wants consistent review of fairness, transparency, and acceptable use across projects.
What governance mechanism best addresses this need?

A. A project management office with AI project templates
B. A data quality council reporting to the CIO
C. An AI ethics or responsible AI review board with defined authority
D. A model risk management policy issued by internal audit

Answer: C

Explanation:
A responsible AI review board provides cross-project evaluation of ethical, fairness, and acceptable-use questions with defined authority to approve, condition, or halt initiatives. This creates consistency that project-level controls alone cannot achieve.

NEW QUESTION # 326
A project manager is overseeing the transition of a company's legacy system to a new AI-driven solution. The team has identified multiple cognitive patterns required for different aspects of the system. However, the project manager is concerned about overcomplicating the transition. When activity should be performed first?

A. Identify parts of the project that do not require intelligent systems.
B. Establish a phased approach targeting one pattern at a time.
C. Consolidate all cognitive patterns into a single iteration.
D. Train employees on all identified cognitive patterns simultaneously.

Answer: A

Explanation:
Identifying parts of the project that do not require intelligent systems should be done first because it separates noncognitive automation needs from true AI cognitive patterns. This reduces unnecessary complexity and helps the team apply AI only where learning, reasoning, prediction, or decision support is actually required.

NEW QUESTION # 327
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