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PMI CPMAI Exam Syllabus Topics:

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

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PMI Cognitive Project Management in AI (PMI-CPMAI) Sample Questions (Q116-Q121):

NEW QUESTION # 116
The project team at an IT services company is working on an AI-based customer support chatbot.
To help ensure the chatbot functions effectively, they need to define the required data. Which method meets the project requirements?

A. Developing a new script based on anticipated customer queries
B. Gathering historical customer interaction logs for training data
C. Using synthetic data generated from sample customer conversations
D. Integrating feedback from beta customers to refine the model

Answer: B

Explanation:
Gathering historical customer interaction logs provides real examples of customer questions, language patterns, issues, intents, and support responses. This helps define and supply the data needed to train an effective customer support chatbot.

NEW QUESTION # 117
Your team is looking to develop an RPA bot to help assist call center agents while on providing support. What type of bot should your team be creating?

A. RPA is not the right solution to this problem
B. Augmented Intelligence
C. Attended bot
D. Unattended bot

Answer: C

Explanation:
Attended bots work alongside human agents in real-time, assisting call center agents during support interactions.

NEW QUESTION # 118
You're working with an inexperienced team and this is all their first AI project. You're trying to work on a supervised learning binary classification problem to determine if emails are spam or not.
What is the best approach for this project?

A. Pick a neural network algorithm since you know this works well for supervised learning approaches
B. Pick a simple algorithm such as Gaussian mixture
C. Pick an ensemble method since you're not sure which algorithm will perform best
D. Pick a simple algorithm such as naive bayes

Answer: D

NEW QUESTION # 119
In the early stages of an AI project, the team needs to determine the types of environments and devices where the AI solution will be used. This information is crucial to ensure a successful implementation. Which action should the project manager implement first?

A. Perform a technical requirements audit.
B. Draft a detailed usage scenario analysis.
C. Hold workshops with end users to gather feedback.
D. Conduct comprehensive user experience research.

Answer: B

Explanation:
Drafting a detailed usage scenario analysis identifies where, how, by whom, and on which devices the AI solution will be used. This provides the practical context needed to define environments, user interactions, deployment needs, and implementation constraints early in the project.

NEW QUESTION # 120
A project involves integrating AI systems across multiple departments, each with different access levels. This complex Al project has presented the project manager with significant issues related to data misuse. The project team has been focused on their ethics guidelines but continues to experience data misuse. The project involves different regional data protection regulations which further increases the complexity. What issue will cause these challenges to occur?

A. Lack of a detailed plan addressing a governance strategy
B. Limited awareness of explainability requirements
C. Overlooking algorithmic bias and fairness concerns
D. Failure to implement robust encryption for data security

Answer: A

Explanation:
A detailed governance strategy is needed to define how data is accessed, used, shared, monitored, and controlled across departments and regions. Ethics guidelines alone are not enough when the project involves multiple access levels, potential misuse, and varying data protection regulations.

NEW QUESTION # 121
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