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PMI CPMAI Exam Syllabus Topics:

Section	Weight	Objectives
Data Preparation for AI		- Data cleaning and transformation 1. Data quality assurance 2. Feature engineering and preparation
AI Model Development and Iteration		- Model building and validation 1. Machine learning / generative AI model development 2. Iterative delivery approach
Identify Business Needs and Solutions	26%	- Problem framing and business alignment 1. Feasibility and ROI analysis 2. Define AI business objectives
Data for AI		- Data identification and governance 1. Data compliance and ethics 2. Data sourcing and selection
AI System Testing and Evaluation		- Model evaluation and monitoring 1. Performance evaluation and drift detection 2. Explainability and reliability assessment
AI Operationalization and Governance		- Deployment and lifecycle management 1. Continuous improvement and monitoring 2. AI governance and responsible AI

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PMI Cognitive Project Management in AI (PMI-CPMAI) Sample Questions (Q141-Q146):

NEW QUESTION # 141
A financial services firm is operationalizing an AI-driven fraud detection system. The project manager needs to ensure the tool complies with relevant data privacy laws while providing secure data access to only authorized personnel. What is an effective technique to address these requirements?

A. Developing a comprehensive data classification policy (DCP)
B. Implementing real-time data verification (RTDV) processes
C. Utilizing role-based access control (RBAC) to limit data access
D. Conducting a privacy impact assessment (PIA) to identify risks

Answer: C

Explanation:
Utilizing role-based access control limits sensitive fraud detection data to authorized personnel based on their job responsibilities. This supports secure access management and helps meet data privacy requirements by preventing unnecessary or inappropriate access.

NEW QUESTION # 142
An AI-powered chatbot occasionally generates inappropriate or harmful responses despite strong training performance. The organization wants to reduce risks while maintaining usability. What is the most effective mitigation strategy for this issue?

A. Accelerate deployment cycles
B. Remove monitoring mechanisms
C. Increase dataset size
D. Implement guardrails and content filters

Answer: D

Explanation:
Guardrails control outputs and reduce harmful responses.

NEW QUESTION # 143
You've built your model and now need to see if it actually works as expected. In which phase of CPMAI is this done?

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

Answer: F

Explanation:
Phase V of CPMAI involves model evaluation and testing to validate if the model performs as expected before deployment.

NEW QUESTION # 144
A deployed AI system meets all technical acceptance criteria, but six weeks after release, usage data shows that fewer than 10 percent of intended users are engaging with it.
What was most likely inadequate?

A. Infrastructure capacity planning
B. Model evaluation rigor
C. Data quality assessment
D. Organizational change management and user adoption planning

Answer: D

Explanation:
Technical success does not produce business value unless users adopt the system in their workflow. Adoption requires training, workflow integration, communication, and management of concerns about how the system affects existing roles.

NEW QUESTION # 145
A healthcare organization is developing an Al model to predict patient outcomes. The project manager needs to address issues of missing or incorrect data before training the model. What is an effective method to help ensure data quality?

A. Applying predictive data algorithms
B. Implementing an AI data needs assessment
C. Enhancing with data augmentation techniques
D. Performing systematic data validation

Answer: D

Explanation:
Performing systematic data validation helps identify and correct missing, inaccurate, inconsistent, or incomplete patient data before model training, improving the reliability of the data used to predict patient outcomes.

NEW QUESTION # 146
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