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PMI CPMAI Exam Syllabus Topics:

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

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PMI Cognitive Project Management in AI (PMI-CPMAI) Sample Questions (Q142-Q147):

NEW QUESTION # 142
Your team is looking to develop an RPA bot to help assist call center agents while on providing support. What type of bot should your team be creating?

Answer: B


NEW QUESTION # 143
A government agency is developing an AI system to predict infrastructure failures. The data team needs to ensure that the dataset includes historical maintenance records, environmental data, and equipment usage logs. However, they are encountering issues with data from multiple sources being in different formats. Which two methods should be applied to meet the data team's objectives? (Choose two.)

Answer: A,E

Explanation:
A multiformat data integration tool helps combine maintenance records, environmental data, and equipment usage logs from different systems and formats. A data normalization process then standardizes those inputs into consistent structures and values so the dataset can be used reliably for AI model development.


NEW QUESTION # 144
Your company is insisting on running an automation project and applying AI best practices and methodologies to the project. You understand that automating things is just the act of using machines to repeat tasks, and does not require AI to achieve results. You think it is overkill but the project moves forward as planned. What would likely have helped avoid this conflict?

Answer: A

Explanation:
Ensuring that all team members understand the fundamental differences between automation and AI systems helps align expectations and avoids conflicts about the appropriate use of AI best practices.


NEW QUESTION # 145
Your team is starting a new facial recognition project and you want to ensure that the project is being done with Trustworthy AI in mind.
At what phase of CPMAI would Trustworthy AI be considered?

Answer: C


NEW QUESTION # 146
One of the key elements of a data-centric methodology is the data requirements phase. During CPMAI Phase II, several unexpected issues have developed and are now threatening the data collection efforts. What course of action might make the issue worse?

Answer: B

Explanation:
Expanding the scope when facing data collection issues can worsen problems by increasing complexity and resource demands, making it harder to manage and deliver the project successfully.


NEW QUESTION # 147
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