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PMI CPMAI Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Managing AI	8%	- Risk management in AI projects - Managing AI project teams and resources - Stakeholder management
Topic 2: CPMAI Methodology	41%	- Phase II: Data Identification & Curation 1. Assess data readiness and quality 2. Identify data needs - Phase VI: Iteration & Monitoring 1. Continuous monitoring and iteration 2. Retraining and model drift management - Phase V: Operationalization 1. Define success criteria and monitoring 2. Plan for deployment and integration - 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
Topic 3: AI Fundamentals	16%	- Types of AI and Machine Learning - Concepts and terminology of Artificial Intelligence - AI capabilities and limitations
Topic 4: Trustworthy AI	9%	- Ethical considerations and bias - Transparency and explainability - Privacy and security
Topic 5: Machine Learning	13%	- Supervised, Unsupervised, and Reinforcement Learning - Deep Learning and Neural Networks - Algorithms and models (e.g., NLP, Computer Vision)
Topic 6: Data for AI	13%	- Data strategy and governance - DataOps concepts - Data preparation and preprocessing

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PMI Cognitive Project Management in AI (PMI-CPMAI) Sample Questions (Q268-Q273):

NEW QUESTION # 268
You have been brought on to manage a recognition project, specifically an image recognition project, for an Autonomous Retail application. You know that you need to make sure you have sufficient data for this project.
What's the best way to approach this?

A. Take inventory of all data your team has and use the relevant data
B. Take all the data your company has as well as purchase additional external data
C. Take all the existing data you have and apply it to this project
D. Take inventory of all data your company has and use the relevant data

Answer: D

NEW QUESTION # 269
An aerospace firm is developing an AI system for predictive maintenance of their aircraft. The project team needs to define the required data to train the model. Which activity should the project manager implement?

A. Conducting a pilot test with a small dataset
B. Setting up real-time data streaming from aircraft sensors
C. Implementing data cleaning and preprocessing routines
D. Developing a comprehensive data collection strategy

Answer: D

Explanation:
Developing a comprehensive data collection strategy defines what aircraft sensor, maintenance, operational, failure, and environmental data is required to train the predictive maintenance model.
This ensures the model is built from relevant and sufficient data before later preparation, streaming, or testing activities begin.

NEW QUESTION # 270
A product manager proposes using customer data collected for marketing purposes to train a new recommendation model without updating user consent agreements. The AI team is ready to proceed for faster delivery. What should the project manager do next?

A. Anonymize the data and continue immediately
B. Escalate to legal and compliance teams before proceeding
C. Ignore the issue and proceed
D. Approve the request to meet deadlines

Answer: B

Explanation:
Using data beyond consent violates privacy regulations; legal review is mandatory.

NEW QUESTION # 271
A project manager is evaluating the readiness of the team for an AI operationalization project.
They have identified a lack of experience with AI and data knowledge. If this issue is neglected, what is the outcome?

A. The project will face significant delays due to learning curves.
B. The project will achieve its objectives with external consultant help.
C. The project will require frequent adjustments to the AI models.
D. The project will proceed smoothly with existing knowledge.

Answer: A

Explanation:
A lack of AI and data knowledge creates skill gaps that slow down planning, data preparation, model validation, deployment, and troubleshooting. If not addressed, the team will likely experience delays as they learn required concepts and practices during execution.

NEW QUESTION # 272
Senior management has tasked your group to analyze a data set to uncover insights into the dat

A. Data Integration
B. Data Mining or Data Analytics
C. Data preparation
D. What is the best approach to use to do this?
E. Data Governance

Answer: C

NEW QUESTION # 273
......

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