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PMI CPMAI Exam Syllabus Topics:

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

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100% Pass Quiz 2026 PMI CPMAI: Cognitive Project Management in AI (PMI-CPMAI) Marvelous Latest Test Simulations

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PMI Cognitive Project Management in AI (PMI-CPMAI) Sample Questions (Q153-Q158):

NEW QUESTION # 153
During CPMAI Methodology Phase IV: Model Development, which of the following is not done during this phase?

Answer: B

Explanation:
Model selection typically occurs after development, during the evaluation and validation phase, rather than during the Model Development phase where algorithms are selected, trained, and tuned.


NEW QUESTION # 154
A project manager at a consulting firm is tasked with facilitating the creation of an AI-driven predictive maintenance system for a manufacturing client. Identifying data sources and locations is crucial for the project's success. Which method will help to ensure the project manager meets the objectives?

Answer: C

Explanation:
Creating a detailed data flow diagram helps identify where data comes from, where it is stored, how it moves between systems, and how it will be used in the predictive maintenance solution.
This directly supports locating and validating the required data sources for the AI project.


NEW QUESTION # 155
In what way would you be using Generative AI if you used the results of the Generative AI solution to improve and accelerate your job?

Answer: B


NEW QUESTION # 156
Your team has collected petabytes of data for your AI project. As the project lead, you understand this is too much data to use for this iteration of the project. What is the best course of action to take with this data?

Answer: A

Explanation:
Data selection and attribute pruning help reduce dataset size and complexity by focusing on relevant features and samples, making model training more efficient without losing important information.


NEW QUESTION # 157
A transportation company is preparing data for an AI model to optimize fleet management. The project team is working with large amounts of structured and unstructured data. If the project manager avoids addressing the variety of data during preparation, what will be the result?

Answer: A

Explanation:
Failing to address the variety of structured and unstructured data can leave the model with inconsistent formats, incomplete representations, and unusable inputs. This reduces the model's ability to learn accurate fleet management patterns and leads to weaker performance.


NEW QUESTION # 158
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