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PMI CPMAI Exam Syllabus Topics:

Section	Weight	Objectives
Data for AI	13%	- Data strategy and governance - Data preparation and preprocessing - DataOps concepts
Trustworthy AI	9%	- Ethical considerations and bias - Transparency and explainability - Privacy and security
Managing AI	8%	- Stakeholder management - Managing AI project teams and resources - Risk management in AI projects
CPMAI Methodology	41%	- 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 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. Oversee AI/ML model QA/QC 2. Evaluate model performance against metrics - Phase I: Problem Identification 1. Evaluate initial AI feasibility 2. Identify business needs and solutions
AI Fundamentals	16%	- AI capabilities and limitations - Concepts and terminology of Artificial Intelligence - Types of AI and Machine Learning
Machine Learning	13%	- Algorithms and models (e.g., NLP, Computer Vision) - Deep Learning and Neural Networks - Supervised, Unsupervised, and Reinforcement Learning

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PMI Cognitive Project Management in AI (PMI-CPMAI) Sample Questions (Q93-Q98):

NEW QUESTION # 93
Humans have an innate capability that cognitive machines have not been able to replicate. What illustrates the core aspect of human intelligence related to the ability to adapt to new situations?

A. Performing tasks based on symbolic reasoning.
B. Processing large volumes of information quickly.
C. Manipulating the environment through random action.
D. Applying past experiences to solve unfamiliar problems.

Answer: D

Explanation:
Human intelligence is strongly characterized by adaptive learning-the ability to transfer knowledge and lessons from prior experiences to unfamiliar situations and use them to solve new problems effectively.

NEW QUESTION # 94
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. Compliance with data regulations
B. Ability to handle increased loads
C. Integration with the existing infrastructure
D. Human oversight requirements

Answer: B

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 # 95
A project team is using a prompt engineering approach to improve AI/machine learning (ML) model outputs. They started with broad questions and then narrowed down the specific elements.
If the team had provided insufficient context, what would be the result?

A. The model would perform more efficiently.
B. The model would generate more creative outputs.
C. The output would include higher accuracy.
D. The responses would lack relevance.

Answer: D

Explanation:
Insufficient context prevents the model from understanding the intended goal, constraints, audience, or required details. As a result, the generated responses are likely to be vague, misaligned, or not relevant to the specific task.

NEW QUESTION # 96
You have been tasked with creating a model that will recommend products based on what other customers have similarly purchased. Which algorithm is the best choice given this situation?

A. Hyperpersonalization
B. Neural Network
C. K-means
D. K Nearest Neighbor

Answer: D

NEW QUESTION # 97
A project manager meets with a customer for initial discussions about an upcoming project. At the end of the meeting, the customer asks the project manager for a rough estimate of the project duration. Based on her experience with three similar projects, the project manager provides an estimate of 8-10 months. What's wrong with this timeframe?

A. It fits into a waterfall timeframe, but not an agile project timeframe
B. It's not accounting for potential project delays
C. It's underestimating the project timeline by 3 months
D. It's not accounting for data preparation timelines

Answer: D

Explanation:
The estimate likely overlooks the significant time required for data preparation, which is a critical and often time-consuming phase in AI projects.

NEW QUESTION # 98
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