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PMI CPMAI Exam Syllabus Topics:

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

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PMI Cognitive Project Management in AI (PMI-CPMAI) Sample Questions (Q196-Q201):

NEW QUESTION # 196
An organization is considering multiple AI solutions to ensure regulatory compliance. What is an effective technique that helps to ensure the AI solution adheres to compliance requirements?

A. Using pretrained models from compliant sources
B. Incorporating compliance rules directly into the algorithms
C. Implementing continuous compliance monitoring
D. Conducting quarterly compliance assessments

Answer: C

Explanation:
Continuous compliance monitoring provides ongoing oversight of the AI solution against applicable regulatory requirements. It helps identify compliance deviations as they occur and supports timely corrective action as regulations, data, models, and system behavior change.

NEW QUESTION # 197
The team is working to build a data preparation pipeline for the conversational chatbot project.
Which phase of CPMAI is this done?

A. Phase I
B. Phase VI
C. Phase V
D. Phase III
E. Phase IV
F. Phase II

Answer: F

Explanation:
Phase II of CPMAI focuses on data preparation, including building pipelines for data collection, cleaning, and labeling needed for model training.

NEW QUESTION # 198
An IT services company is working on a project to develop an AI-based customer support system.
During data preparation, the project manager needs to clean and transform customer interaction logs. What is an effective technique to handle any missing data?

A. Fill missing values with zeros without analysis.
B. Ignore missing data if it seems insignificant.
C. Duplicate existing data to fill in missing gaps.
D. Remove records with missing values if minimal.

Answer: D

Explanation:
Removing records with missing values can be effective when the amount of missing data is minimal and its removal will not distort the dataset. This helps maintain clean and reliable customer interaction logs before transformation and model training.

NEW QUESTION # 199
Using machine learning and other cognitive approaches to understand how to take past / existing behavior and predict future outcomes or help humans make decisions about future outcomes using insight learned from past behavior / interactions / data is a core part to which pattern(s) of AI?

A. Recognition Pattern
B. Predictive Analytics & Decision Support
C. Predictive Analytics & Decision Support and Patterns and Anomalies
D. Goal Driven Systems

Answer: B

Explanation:
Using machine learning to analyze past behavior and predict future outcomes or assist decision- making is central to the Predictive Analytics & Decision Support pattern in AI.

NEW QUESTION # 200
In an aerospace manufacturing project, engineers are preparing data to train an AI system for predictive maintenance. They need to transform the data from multiple sensors and ensure it is consistent and accurate before building the model. What should the project manager do to handle the inconsistencies?

A. Implement a validation protocol for sensor data.
B. Identify and reconcile conflicting data points.
C. Enhance the current data with additional sources.
D. Use data augmentation techniques to fill the gaps.

Answer: B

Explanation:
Identifying and reconciling conflicting data points directly addresses inconsistencies in sensor data before model training. This helps ensure the predictive maintenance model is trained on accurate, consistent, and reliable inputs.

NEW QUESTION # 201
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