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PMI CPMAI Exam Syllabus Topics:

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

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PMI Cognitive Project Management in AI (PMI-CPMAI) Sample Questions (Q309-Q314):

NEW QUESTION # 309
You just joined a new company and they want to start their first AI project. Senior management thinks the best approach is to just buy AI from a vendor. You know that AI is something you do, not something you buy. What is your next best course of action to address this?

A. Share prior experiences with how your last team addressed this problem and their data quality issues
B. Share prior experiences with how your last team addressed this problem and how you solved it
C. Help senior management do research on AI vendors
D. Say nothing and let the team figure it out for themselves

Answer: B

Explanation:
Sharing your prior experiences with how your previous team approached AI projects helps educate senior management on the complexities of AI implementation and the importance of doing AI rather than just buying it.

NEW QUESTION # 310
You're working on a computer vision application and realize that you do not have enough real world data for the project. You need additional data created to support your training needs.
Specifically, the images you need are of people in different poses. What is the best way to obtain this data?

A. Make use of this data by having employees pose in the positions required
B. Make use of data from different departments
C. Make use of Synthetic Training Data
D. Make use of this data from surveillance footage

Answer: C

NEW QUESTION # 311
You are working with a dataset that has a high number of dimensions. You're running into issues because some dimensions don't have enough real examples to properly train the systems for predictable results. What's your best course of action?

A. Try to improve the quality of your data through more preparation
B. Try to get additional information from project lead to see how many examples per dimension are needed
C. Keep going as planned and the problem will eventually correct itself
D. Try to get additional data - at least 5 training examples for each dimension in the representation

Answer: D

Explanation:
Obtaining sufficient training examples for each dimension helps ensure the model can learn reliable patterns and improve prediction accuracy in high-dimensional datasets.

NEW QUESTION # 312
A government agency is adopting an AI/machine learning (ML) model to analyze large sets of public data for policy making. It is crucial that the project team ensures the accuracy of the model's predictions.
If the project team needs to validate the model, which action should they perform?

A. Conduct a single comprehensive validation.
B. Implement continuous integration testing.
C. Utilize a diverse set of test cases.
D. Ensure adherence to coding standards.

Answer: C

Explanation:
Using a diverse set of test cases validates whether the model performs accurately across different data conditions, scenarios, and population segments. This provides a more reliable assessment of predictive performance and helps reveal weaknesses that could be missed with limited or homogeneous testing.

NEW QUESTION # 313
A project manager is evaluating whether an AI model is ready for operationalization. The project team has ensured the model meets accuracy and performance requirements and has conducted extensive testing. However, uncertainty about the model's operationalization in different environments and its compliance with regulatory standards still exists. What should the team do next?

A. Reassess the project's data preparation phase.
B. Evaluate the model's adaptability to diverse conditions.
C. Implement additional noncognitive solutions.
D. Consider retraining the model for better performance.

Answer: B

Explanation:
Evaluating the model's adaptability to diverse conditions helps determine whether it can operate reliably across different environments while still meeting deployment, governance, and regulatory expectations.

NEW QUESTION # 314
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