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PMI CPMAI Exam Syllabus Topics:

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

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PMI Cognitive Project Management in AI (PMI-CPMAI) Sample Questions (Q11-Q16):

NEW QUESTION # 11
You're working on a project and are working with personally identifiable information (PII). What's the best approach to take when it comes to collecting and using this data?

A. Implement a new data privacy policy
B. Store the data in a data warehouse
C. Use noise reduction techniques to reduce all forms of data noise
D. If this data is not needed, use Data anonymization techniques to remove it before feeding to models

Answer: D

NEW QUESTION # 12
As an organization building an AI solution for your current customers based in NYC, but with possible plans for future expansion, how should you handle worldwide AI laws and regulations?

A. Make sure to follow relevant data, privacy, and other important laws as it pertains to NYC
B. You're too small of an organization to be worried about laws at the moment
C. Make sure to follow relevant data, privacy, and other important laws as it pertains to the United States
D. Make sure to follow relevant data, privacy, and other important laws both in the US and where you're likely to expand to in the coming year

Answer: D

NEW QUESTION # 13
An organization aims to improve transparency and trust in its AI systems. Stakeholders request documentation outlining model purpose, limitations, risks, and performance characteristics. Which artifact should the project manager ensure is created and maintained?

A. Model card
B. Deployment script
C. Source code repository
D. Data pipeline diagram

Answer: A

Explanation:
Model cards provide transparency and communicate risks and limitations.

NEW QUESTION # 14
Which of the following best describes the technical definition of Machine Learning?

A. A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P if its performance at tasks in T, as measured by P, improves with experience E.
B. An approach to using increasing levels of intelligence to solve greater cognitive needs from unintelligent automation to autonomous business process.
C. The use of computing technology to enable machines to gain cognitive intelligence.
D. The application of pre-defined rules and algorithms to solve complex problems.

Answer: A

NEW QUESTION # 15
Your team is tasked with selecting an algorithm for a supervised learning classification project.
Which algorithm might you choose?

A. Q learning
B. K-means
C. Gaussian mixture
D. K-nearest neighbor

Answer: D

Explanation:
K-nearest neighbor is a supervised learning algorithm used for classification tasks, making it suitable for supervised classification projects.

NEW QUESTION # 16
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