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PMI CPMAI Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Data for AI		- Data identification and governance 1. Data sourcing and selection 2. Data compliance and ethics
Topic 2: AI Model Development and Iteration		- Model building and validation 1. Iterative delivery approach 2. Machine learning / generative AI model development
Topic 3: AI Operationalization and Governance		- Deployment and lifecycle management 1. AI governance and responsible AI 2. Continuous improvement and monitoring
Topic 4: AI System Testing and Evaluation		- Model evaluation and monitoring 1. Performance evaluation and drift detection 2. Explainability and reliability assessment
Topic 5: Data Preparation for AI		- Data cleaning and transformation 1. Feature engineering and preparation 2. Data quality assurance
Topic 6: Identify Business Needs and Solutions	26%	- Problem framing and business alignment 1. Feasibility and ROI analysis 2. Define AI business objectives

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PMI Cognitive Project Management in AI (PMI-CPMAI) Sample Questions (Q37-Q42):

NEW QUESTION # 37
You are working for a large multinational organization and have been assigned to a new project.
For your new ML project, you need to make sure you're managing data privacy and security as you're working with sensitive customer data. What critical security issues do you need to make sure you address? (Choose all that apply.)

A. Securely storing all data collected for training purposes
B. Securing model data and metadata
C. Compliance with Data Privacy Laws even if they are out of your physical jurisdiction
D. Securing data at rest

Answer: A,B,C,D

Explanation:
Managing sensitive customer data requires comprehensive security measures, including compliance with data privacy laws regardless of location, securing both model data and metadata, protecting data at rest, and ensuring secure storage of all training data.

NEW QUESTION # 38
A team is running a forecasting project and wants to use previous user data to better predict future outcomes. However, the team does not have access to all the data they need.
Which action should the project manager take?

A. Move forward while anticipating data access is given when needed. An iterative approach provides the ability to return to steps as needed later on.
B. Move forward cautiously with the understanding that there may be a need for a pause mid-project.
C. Move forward in order to remain on schedule with the project.
D. Do not move forward until access is given to all the necessary data.

Answer: A

Explanation:
An iterative approach allows the team to begin with the available data while revisiting earlier project activities as additional data becomes accessible. This supports progress without assuming that every data dependency must be resolved before the forecasting project can start.

NEW QUESTION # 39
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. Increase dataset size
B. Accelerate deployment cycles
C. Remove monitoring mechanisms
D. Implement guardrails and content filters

Answer: D

Explanation:
Guardrails control outputs and reduce harmful responses.

NEW QUESTION # 40
A healthcare organization plans to use an AI solution to predict patient readmissions. The data science team needs to identify data sources and ensure data quality. Which method will meet the project team's objectives?

A. Using data profiling tools to assess data completeness
B. Setting up a continuous integration pipeline for real-time data validation
C. Implementing data augmentation techniques to fill missing values
D. Operationalizing a data catalog to maintain metadata standards

Answer: D

Explanation:
Operationalizing a data catalog helps identify available data sources and maintain consistent metadata standards. This supports data discovery, ownership, lineage, relevance, and quality management for patient readmission prediction.

NEW QUESTION # 41
Your organization wants to keep an eye on AI systems for Governance purposes. What are the most crucial things to consider? (Choose all that apply.)

A. AI System testing requirements
B. Continuous System monitoring
C. Algorithm selection
D. Data source identification
E. Vendor procurement methods
F. Human chain of accountability
G. Key Performance Indicators (KPIs)
H. ROI determination

Answer: A,B,F,G

Explanation:
Effective AI governance requires robust system testing, continuous monitoring to detect issues, clear human accountability for decisions, and KPIs to measure system performance and impact.

NEW QUESTION # 42
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