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PMI CPMAI Exam Syllabus Topics:

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

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PMI Cognitive Project Management in AI (PMI-CPMAI) Sample Questions (Q25-Q30):

NEW QUESTION # 25
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 both in the US and where you're like to expand to in the coming year
B. Make sure to follow relevant data, privacy, and other important laws as it pertains to the United States
C. Make sure to follow relevant data, privacy, and other important laws as it pertains to NYC
D. You're too small of an organization to be worried about laws at the moment

Answer: A

Explanation:
It is essential to comply with relevant data, privacy, and AI laws not only in the current operating region but also in regions targeted for expansion, ensuring legal readiness and avoiding future compliance risks.

NEW QUESTION # 26
An AI project team has prepared the data and is ready to proceed with model development.
Which action should the project manager perform next?

A. Prepare a report on the model's scalability.
B. Conduct a final assessment of the data quality.
C. Document the performance metrics for the model.
D. Ensure go/no-go questions have well-defined answers.

Answer: B

Explanation:
Conducting a final assessment of data quality ensures the prepared data is complete, accurate, relevant, consistent, and ready for model development. This helps prevent poor-quality inputs from affecting training results and model reliability.

NEW QUESTION # 27
Your team is working on a new facial recognition application. Since this technology has the potential to be mis-used you think it's important to set guidelines for the proper use of this application and you want to make sure the AI system is built for some positive purpose. What area of Trustworthy AI does this best fall under?

A. Responsible AI
B. Transparent AI
C. Governed AI
D. Explainable AI

Answer: A

Explanation:
Responsible AI focuses on ensuring AI systems are developed and used ethically, with proper guidelines and safeguards to promote positive and fair outcomes.

NEW QUESTION # 28
A telecommunications company plans to operationalize a new AI-powered network optimization tool. The project manager needs to identify and map out all necessary tasks and associated resources. Which activity should the project manager perform?

A. Using a Gantt chart to schedule and monitor resource deployment timelines
B. Conducting a feasibility study to assess the technical and financial viability
C. Applying an earned value management (EVM) methodology
D. Developing a work breakdown structure (WBS)

Answer: D

Explanation:
Developing a work breakdown structure decomposes the AI operationalization work into manageable tasks and connects those tasks to the required resources, making it easier to define scope, responsibilities, and execution needs.

NEW QUESTION # 29
Major factors for the project you are currently working on is around the training time, cost, and complexity of training your models. Which algorithm is not the best choice given these constraints?

A. Naive Bayes
B. Support Vector Machines (SVM)
C. Neural Networks
D. Gaussian Mixture

Answer: A

NEW QUESTION # 30
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