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PMI CPMAI Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Trustworthy AI9%- Ethical considerations and bias
- Transparency and explainability
- Privacy and security
Topic 2: CPMAI Methodology41%- Phase III: Algorithm Selection & Model Training
  • 1. Manage model training and configuration
  • 2. Oversee AI/ML model technique selection
- Phase I: Problem Identification
  • 1. Evaluate initial AI feasibility
  • 2. Identify business needs and solutions
- Phase II: Data Identification & Curation
  • 1. Assess data readiness and quality
  • 2. Identify data needs
- Phase V: Operationalization
  • 1. Define success criteria and monitoring
  • 2. Plan for deployment and integration
- Phase VI: Iteration & Monitoring
  • 1. Retraining and model drift management
  • 2. Continuous monitoring and iteration
- Phase IV: Model Evaluation
  • 1. Oversee AI/ML model QA/QC
  • 2. Evaluate model performance against metrics
Topic 3: Machine Learning13%- Deep Learning and Neural Networks
- Algorithms and models (e.g., NLP, Computer Vision)
- Supervised, Unsupervised, and Reinforcement Learning
Topic 4: Data for AI13%- DataOps concepts
- Data strategy and governance
- Data preparation and preprocessing
Topic 5: AI Fundamentals16%- Concepts and terminology of Artificial Intelligence
- Types of AI and Machine Learning
- AI capabilities and limitations
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 (Q312-Q317):

NEW QUESTION # 312
Different AI project team members are responsible for various parts of the project, both cognitive and non-cognitive. The project manager needs to ensure effective accountability documentation.
Which method will help to ensure accurate documentation?

Answer: D

Explanation:
Using a centralized documentation system gives all team members a single source of truth for responsibilities, decisions, progress, and accountability across both cognitive and noncognitive project work. This helps ensure documentation remains accurate, accessible, and consistently maintained.


NEW QUESTION # 313
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: B

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 # 314
Your team has been asked to summarize and highlight patterns in historical purchasing data, identifying prior performance metrics and patterns. What type of analytics is most appropriate to apply for this need?

Answer: B


NEW QUESTION # 315
In an aerospace manufacturing project, engineers are preparing data to train an AI system for predictive maintenance. They need to transform the data from multiple sensors and ensure it is consistent and accurate before building the model. What should the project manager do to handle the inconsistencies?

Answer: D

Explanation:
Identifying and reconciling conflicting data points directly addresses inconsistencies in sensor data before model training. This helps ensure the predictive maintenance model is trained on accurate, consistent, and reliable inputs.


NEW QUESTION # 316
A telecommunications company is implementing an AI solution to optimize network performance.
The project team needs to prepare the data for the AI system by addressing data format inconsistencies. Which method should the project manager use?

Answer: B

Explanation:
PMI's CPMAI/PMI-CPMAI guidance places "data preparation and transformation" at the center of getting data into a usable state for model development and operations. The CPMAI v7 outline explicitly includes coordinating data preparation activities such as formulating data preparation requirements and performing data cleansing and enhancement--work that directly addresses inconsistent formats. In addition, CPMAI v7 lists "Executing Data Preparation and Transformation," including methods to improve data quality and accuracy and to clean/enhance data for optimal AI performance. When the issue is format inconsistency (e.g., mismatched schemas, units, encodings, timestamp formats), the PMI-aligned response is to define and execute the required transformation steps (normalize formats, standardize fields, convert units, align timestamps, encode categories) so the dataset meets the model and pipeline requirements.


NEW QUESTION # 317
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