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PMI CPMAI Exam Syllabus Topics:

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

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PMI Cognitive Project Management in AI (PMI-CPMAI) Sample Questions (Q306-Q311):

NEW QUESTION # 306
An insurance company is selecting an AI approach to automate simple claim approvals for low- risk cases. The organization wants the system to take actions with minimal human intervention based on predefined policies. Which AI capability best fits?

Answer: C

Explanation:
In PMI's Seven Patterns of AI, capability selection depends on whether the system is primarily advising humans or acting on their behalf. When the goal is to automate operational actions-- approving or routing claims under policy constraints with minimal human intervention--the capability aligns with autonomous systems, which emphasize automated execution within defined rules, safeguards, and operational boundaries. Predictive analytics (B) can score risk, but it typically supports decision support; autonomous systems extend this by taking actions automatically according to governance-approved policies. PMI-CPMAI's responsible and trustworthy AI principles reinforce that higher-autonomy use cases require stronger controls: clear escalation paths, contingency plans, monitoring, and audit trails to ensure accountability for automated decisions.


NEW QUESTION # 307
An organization is considering multiple AI solutions to ensure regulatory compliance. What is an effective technique that helps to ensure the AI solution adheres to compliance requirements?

Answer: B

Explanation:
Continuous compliance monitoring provides ongoing oversight of the AI solution against applicable regulatory requirements. It helps identify compliance deviations as they occur and supports timely corrective action as regulations, data, models, and system behavior change.


NEW QUESTION # 308
Your team is looking to develop an RPA bot to help with back-office processes such as data entry. What type of bot should your team be creating?

Answer: A

Explanation:
An unattended bot operates autonomously without human intervention, making it ideal for back- office tasks like data entry that are repetitive and do not require user interaction.


NEW QUESTION # 309
Enhancing and cleaning data is an important action during which phase of CPMAI?

Answer: E

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 # 310
Your team is working on a project and is running into some issues. You need someone on the team who is able to solve problems in environments of uncertainty, can deal with failure, and has the math and data visualization skills needed to communicate the results with others so the issues can get resolved.
What role would be most suited for this?

Answer: A


NEW QUESTION # 311
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