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PMI CPMAI Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Trustworthy AI	9%	- Transparency and explainability - Ethical considerations and bias - Privacy and security
Topic 2: Machine Learning	13%	- Algorithms and models (e.g., NLP, Computer Vision) - Deep Learning and Neural Networks - Supervised, Unsupervised, and Reinforcement Learning
Topic 3: Managing AI	8%	- Risk management in AI projects - Managing AI project teams and resources - Stakeholder management
Topic 4: CPMAI Methodology	41%	- Phase III: Algorithm Selection & Model Training 1. Oversee AI/ML model technique selection 2. Manage model training and configuration - 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 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 IV: Model Evaluation 1. Oversee AI/ML model QA/QC 2. Evaluate model performance against metrics
Topic 5: Data for AI	13%	- Data strategy and governance - DataOps concepts - Data preparation and preprocessing
Topic 6: AI Fundamentals	16%	- AI capabilities and limitations - Concepts and terminology of Artificial Intelligence - Types of AI and Machine Learning

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PMI Cognitive Project Management in AI (PMI-CPMAI) Sample Questions (Q90-Q95):

NEW QUESTION # 90
During CPMAI Phase II of your project, your team is going through their data collection needs.
One team member wants to make use of pre-trained models while another member is adamantly against it.
As the project lead, what should you do?

A. Evaluate your data and see if using pre-trained models make sense. If so, have the team do research to find the ones that best suit your project.
B. Evaluate your data and use only what you have and build all models in house.
C. Have one team build all models in-house and the other team use pre-trained models and see which team's models perform better.
D. Evaluate your data and see if using pre-trained models make sense. If so, have the team see what pre-trained models your company already owns and use those.

Answer: A

NEW QUESTION # 91
An AI model is frequently retrained with new data to improve performance. However, stakeholders report inconsistent predictions across releases, causing confusion and reduced trust in the system. What risk is most likely occurring in this situation?

A. Data leakage
B. Bias elimination
C. Underfitting
D. Model instability

Answer: D

Explanation:
Frequent retraining without controls leads to inconsistent outputs and instability.

NEW QUESTION # 92
You're working with a small inexperienced team on a new ML project. Choosing the best algorithm with the best settings given the training and test data is proving to be very hard for them. You lack the critical data science resources available on your team, and can't wait weeks until a data science resource becomes available to join your team. What's your best course of action?

A. Outsource the project ASAP
B. Put the project on hold until the resources needed become available
C. Use an AutoML solution
D. Find a citizen data scientist to help

Answer: C

Explanation:
AutoML tools automate algorithm selection and hyperparameter tuning, enabling less experienced teams to develop models efficiently without waiting for specialized data science resources.

NEW QUESTION # 93
A project team plans to integrate a third-party foundation model into a customer-facing solution.
The vendor does not disclose training data sources or update schedules.
What risk should the project manager document as highest priority?

A. Integration effort may exceed estimates
B. Vendor pricing may increase over time
C. Unmanaged changes to model behavior and unverifiable data provenance
D. The model may require additional prompt engineering

Answer: C

Explanation:
Undisclosed training data prevents verification of licensing, bias, and compliance exposure, while undisclosed update schedules mean model behavior can change without notice. These risks affect the reliability and legal standing of the deployed solution.

NEW QUESTION # 94
A government agency is using an AI system to analyze public data for policymaking decisions.
The project manager needs to address the risks related to data accuracy, privacy, and misuse.
What represents the highest risk to the agency?

A. The system lacks a transparency process.
B. The user data is stored in an unsecured database.
C. The AI system is not regularly updated with new data.
D. The AI system relies on third-party providers.

Answer: B

Explanation:
Storing user data in an unsecured database creates the highest risk because it directly exposes sensitive information to unauthorized access, privacy violations, misuse, and regulatory noncompliance. For a government AI system, weak data security can undermine public trust and create serious legal and ethical consequences.

NEW QUESTION # 95
......

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