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PMI CPMAI Exam Syllabus Topics:

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

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PMI Cognitive Project Management in AI (PMI-CPMAI) Sample Questions (Q112-Q117):

NEW QUESTION # 112
During the Data Understanding phase, a project team discovers that only 14 months of transaction history is available for a demand forecasting model that must account for multi-year seasonal cycles.
What is the most appropriate action for the project manager?

A. Proceed with modeling and document the limitation in the risk register
B. Reduce the forecast horizon to match the available data
C. Generate synthetic historical data to extend the time series
D. Raise a go/no-go decision point and reassess project feasibility

Answer: D

Explanation:
CPMAI includes formal go/no-go decision points at the end of each phase. When available data cannot support the defined business objective, the correct response is to return to Business Understanding and reassess feasibility rather than proceeding with a model that cannot meet requirements.

NEW QUESTION # 113
A hospital wants to develop a medical records system with the primary goal of minimizing or eliminating paper records. They have identified where the cognitive AI solution will be applied. In addition, business objectives have been quantified and key performance indicators (KPI) have been determined. What else needs to be done to progress to the next Cognitive Project Management for AI (CPMAI) phase?

A. Explore external data sources
B. Determine the project ROI
C. Begin prototype development
D. Create interdepartmental strategies

Answer: B

Explanation:
Determining the project ROI helps confirm that the quantified business objectives and KPIs justify continuing the AI initiative. This supports the business case and provides a measurable basis for progressing to the next CPMAI phase.

NEW QUESTION # 114
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. Attended bot
B. RPA is not the right solution to this problem
C. Augmented Intelligence
D. Unattended bot

Answer: A

NEW QUESTION # 115
A project team is overseeing the data evaluation for an AI model predicting customer churn. They observed that the model's predictions are biased toward a particular class.
What is an effective technique to mitigate this bias?

A. Adjusting the hyperparameters
B. Implementing stratified sampling
C. Increasing the batch size
D. Using synthetic data generation

Answer: B

Explanation:
Stratified sampling preserves appropriate class proportions when creating training or evaluation subsets. This helps ensure that each class is adequately represented, reducing class-related sampling bias and producing a more balanced assessment of the churn model.

NEW QUESTION # 116
A company is using an AI model to screen job applications. Over time, the project team notices that the model disproportionately rejects applications from certain educational institutions. What is the first step the project manager should take?

A. Analyze the historical data for inherent biases against these institutions.
B. Retrain the model with equal representation from all institutions.
C. Incorporate a fairness constraint into the model's objective function.
D. Adjust the model's decision threshold for applications from these institutions.

Answer: A

Explanation:
Analyzing the historical data for inherent biases is the first step because the team must identify whether past hiring patterns, labels, or institutional representation caused the model to learn unfair rejection behavior. Understanding the source of bias is necessary before retraining, changing thresholds, or applying fairness controls.

NEW QUESTION # 117
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