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PMI CPMAI Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: AI Fundamentals16%- Types of AI and Machine Learning
- AI capabilities and limitations
- Concepts and terminology of Artificial Intelligence
Topic 2: Machine Learning13%- Supervised, Unsupervised, and Reinforcement Learning
- Deep Learning and Neural Networks
- Algorithms and models (e.g., NLP, Computer Vision)
Topic 3: Data for AI13%- Data preparation and preprocessing
- Data strategy and governance
- DataOps concepts
Topic 4: CPMAI Methodology41%- Phase V: Operationalization
  • 1. Define success criteria and monitoring
  • 2. Plan for deployment and integration
- Phase II: Data Identification & Curation
  • 1. Assess data readiness and quality
  • 2. Identify data needs
- 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 I: Problem Identification
  • 1. Identify business needs and solutions
  • 2. Evaluate initial AI feasibility
- Phase IV: Model Evaluation
  • 1. Evaluate model performance against metrics
  • 2. Oversee AI/ML model QA/QC
Topic 5: Managing AI8%- Risk management in AI projects
- Managing AI project teams and resources
- Stakeholder management
Topic 6: Trustworthy AI9%- Transparency and explainability
- Privacy and security
- Ethical considerations and bias

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PMI Cognitive Project Management in AI (PMI-CPMAI) Sample Questions (Q135-Q140):

NEW QUESTION # 135
You have been brought on to manage a recognition project, specifically an image recognition project, for an Autonomous Retail application. You know that you need to make sure you have sufficient data for this project. What's the best way to approach this?

Answer: D

Explanation:
Taking inventory of all company data and selecting relevant data ensures you use appropriate, high-quality datasets tailored to the project needs without unnecessary or irrelevant data.


NEW QUESTION # 136
A government agency is implementing an AI-powered tool to enhance data security through anomaly detection. The project manager is assembling the team. To identify the subject matter experts (SMEs) who can provide the best insights and contributions to this project, the project manager needs to consider their experience and expertise in various technical domains. Which method will help identify the qualified data SMEs?

Answer: D

Explanation:
Conducting interviews to assess knowledge in anomaly detection helps identify SMEs who understand the specific data patterns, security signals, abnormal behaviors, and domain context needed to support an AI-powered security anomaly detection project.


NEW QUESTION # 137
Doctors have been utilizing a sophisticated AI-driven cognitive solution to help with diagnosing illnesses. The AI system is integrated with several medical databases. This allowed the AI system to learn from new patient data and adapt to the latest medical knowledge and practices. The final project report indicated that the AI model had degraded over time which impacted reliability and effectiveness. The AI system must comply with healthcare regulations from various countries.
What is the likely cause for the degradation issue?

Answer: D

Explanation:
Data drift occurs when the characteristics of real-world patient data or medical knowledge change over time, causing the model's learned patterns to become less reliable. In a healthcare diagnostic system that continuously adapts to new data and changing medical practices, drift can reduce overall model accuracy and degrade performance.


NEW QUESTION # 138
A government project plans to implement an AI-based fraud detection system and the project team needs to define the success criteria. They identified potential improvements in detection accuracy, reduction in investigation time, and cost savings as key performance indicators (KPIs).
However, they are unsure how to effectively quantify these KPIs. Which two approaches should be used? (Choose two.)

Answer: A,E

Explanation:
Establishing a baseline using historical data comparisons provides a measurable starting point for quantifying improvements in fraud detection accuracy, investigation time, and cost savings.
Continuous performance monitoring then tracks whether the AI system is meeting those KPI targets over time after implementation.


NEW QUESTION # 139
A project manager overseeing an AI project is concerned about potential data privacy violations.
Before progressing further with the project, what should the project manager do to address this concern?

Answer: D

Explanation:
Conducting a thorough compliance audit against applicable data regulations is the appropriate step before progressing because it verifies whether the project's data collection, processing, storage, access, and usage practices meet privacy and regulatory requirements.


NEW QUESTION # 140
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