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PMI CPMAI Exam Syllabus Topics:

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

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PMI Cognitive Project Management in AI (PMI-CPMAI) Sample Questions (Q129-Q134):

NEW QUESTION # 129
Enhancing and cleaning data is an important action during which phase of CPMAI?

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

Answer: F

Explanation:
Enhancing and cleaning data is a key activity during Phase I of the CPMAI (Cognitive Project Management for AI) framework, which focuses on data preparation and exploration to ensure quality input for model development.

NEW QUESTION # 130
A hospital system has been using a chatbot and has received complaints from end users. The end users believe they are speaking to a person but are frustrated when answers do not make sense. To help ensure end users know that they are engaging with an AI chatbot, what should be considered to support transparency?

A. Inclusion of diverse data sets
B. Operationalize advanced algorithms
C. Use of interpretable AI models
D. Disclosure notice with each use

Answer: D

Explanation:
A disclosure notice with each use clearly informs users that they are interacting with an AI chatbot rather than a human. This supports transparency, manages expectations, and helps reduce user frustration when responses are limited or unclear.

NEW QUESTION # 131
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. Utilize a diverse set of test cases.
C. Ensure adherence to coding standards.
D. Implement continuous integration testing.

Answer: B

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 # 132
A telecommunications company is implementing an AI solution to optimize network performance.
The project team needs to prepare the data for the AI system by addressing data format inconsistencies. Which method should the project manager use?

A. Implementing a data governance framework
B. Determining the necessary data transformation steps
C. Creating a comprehensive data quality report
D. Evaluating the potential impact of data breaches

Answer: B

Explanation:
PMI's CPMAI/PMI-CPMAI guidance places "data preparation and transformation" at the center of getting data into a usable state for model development and operations. The CPMAI v7 outline explicitly includes coordinating data preparation activities such as formulating data preparation requirements and performing data cleansing and enhancement--work that directly addresses inconsistent formats. In addition, CPMAI v7 lists "Executing Data Preparation and Transformation," including methods to improve data quality and accuracy and to clean/enhance data for optimal AI performance. When the issue is format inconsistency (e.g., mismatched schemas, units, encodings, timestamp formats), the PMI-aligned response is to define and execute the required transformation steps (normalize formats, standardize fields, convert units, align timestamps, encode categories) so the dataset meets the model and pipeline requirements.

NEW QUESTION # 133
You're working on a project and are working with personally identifiable information (PII). What's the best approach to take when it comes to collecting and using this data?

A. If this data is not needed, use Data anonymization techniques to remove it before feeding to models
B. Store the data in a data warehouse
C. Implement a new data privacy policy
D. Use noise reduction techniques to reduce all forms of data noise

Answer: A

Explanation:
Data anonymization protects individuals' privacy by removing or obfuscating personally identifiable information when such data is not necessary for model training.

NEW QUESTION # 134
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