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PMI CPMAI Exam Syllabus Topics:

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

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PMI Cognitive Project Management in AI (PMI-CPMAI) Sample Questions (Q262-Q267):

NEW QUESTION # 262
Your organization has just rolled out a new image recognition system and is asking all employees to use it. It was trained using images from the ImageNet test set. After a few weeks, users are finding the results are not as expected and are asking for visibility into all the aspects of what went into building an AI system.
What area of Trustworthy AI is being addressed here?

A. Transparent AI
B. Governed AI
C. AI Systemic Transparency
D. Explainable AI
E. Responsible AI

Answer: A

NEW QUESTION # 263
A government project plans to implement an AI-based fraud detection system and the project team needs to define the success criteria. They identified potential improvements in detection accuracy, reduction in investigation time, and cost savings as key performance indicators (KPIs).
However, they are unsure how to effectively quantify these KPIs. Which two approaches should be used? (Choose two.)

A. Establish a baseline using historical data comparisons.
B. Use random benchmarks without industry comparison.
C. Set fixed performance targets based on theoretical models.
D. Implement a continuous performance monitoring system.
E. Rely on only qualitative feedback from stakeholders.

Answer: A,D

Explanation:
Establishing a baseline using historical data comparisons provides a measurable starting point for quantifying improvements in fraud detection accuracy, investigation time, and cost savings.
Continuous performance monitoring then tracks whether the AI system is meeting those KPI targets over time after implementation.

NEW QUESTION # 264
The growth of Big Data has led to a desire to be able to do more to process and extract more value from Big Data. Simply storing data and providing analytics is no longer enough anymore to remain competitive. To keep your organization competitive, you need to:

A. Make sure everyone on the team has an understanding of data, its connections to the organization, and how to extract value from big data to unleash it for competitive advantage.
B. Make sure senior management has deep understanding of big data and how best to extract value from big data to unleash it for competitive advantage.
C. Make sure the technical team has deep understanding of big data and how best to extract value from big data to unleash it for competitive advantage.
D. Make sure all senior leadership is data literate, understands the Vs of big data, data's connections to your specific team, and how to extract value from big data to unleash it for competitive advantage.

Answer: D

Explanation:
Ensuring all senior leadership is data literate and understands big data concepts and their relevance to the organization is critical for strategic decision-making and maintaining a competitive advantage.

NEW QUESTION # 265
Using machine learning and other cognitive approaches to understand how to take past/existing behavior and predict future outcomes or help humans make decisions about future outcomes using insight learned from past behavior/interactions/data is a core part to which pattern(s) of AI?

A. Predictive Analytics & Decision Support and Patterns and Anomalies
B. Predictive Analytics & Decision Support
C. Goal Driven Systems
D. Recognition Pattern

Answer: B

NEW QUESTION # 266
A healthcare organization is developing an Al model to predict patient outcomes. The project manager needs to address issues of missing or incorrect data before training the model. What is an effective method to help ensure data quality?

A. Implementing an AI data needs assessment
B. Performing systematic data validation
C. Enhancing with data augmentation techniques
D. Applying predictive data algorithms

Answer: B

Explanation:
Performing systematic data validation helps identify and correct missing, inaccurate, inconsistent, or incomplete patient data before model training, improving the reliability of the data used to predict patient outcomes.

NEW QUESTION # 267
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