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PMI CPMAI Exam Syllabus Topics:

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

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Reliable PMI CPMAI Test Question - CPMAI Advanced Testing Engine

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PMI Cognitive Project Management in AI (PMI-CPMAI) Sample Questions (Q170-Q175):

NEW QUESTION # 170
A logistics company wants to optimize its delivery routes while adapting to real-time traffic conditions. Which AI pattern or patterns meets these goals?

Answer: D

Explanation:
Predictive analytics supports route optimization by using real-time traffic data, historical delivery patterns, and operational variables to predict delays and recommend better routes as conditions change.


NEW QUESTION # 171
Your team is using a neural network algorithm to generate a Machine Learning Model. What specific artifacts need to be included? (Select all that apply.)

Answer: A,C,D


NEW QUESTION # 172
During CPMAI Phase II, it's important to not only understand the sources of your data but also what data is required for training as well as identifying the features that are required.
When looking to gather data, what approach is best when determining how much data you need?

Answer: C


NEW QUESTION # 173
Accompany wants to determine whether to move forward with an AI project. The project manager has answered and received approval for all questions on technology, data, and business feasibility. Which activity did the project manager perform?

Answer: C

Explanation:
A go/no-go assessment evaluates whether the AI project should proceed by confirming technology readiness, data feasibility, and business value. Approval across these areas indicates the project manager completed the decision checkpoint needed to move forward.


NEW QUESTION # 174
Your team is working on a new loan decision model that takes a number of factors and data points into consideration and then automatically approves or denies a loan. After a month in operation someone does a review and notices that the system is denying a large number of loans from a certain demographic when all other factors from people in other regions (such as age, salary, and credit score) are the same. What is most likely happening here?

Answer: A

Explanation:
Biased datasets can cause the model to discriminate against certain demographics, resulting in unfair or unequal loan denial decisions despite similar attributes.


NEW QUESTION # 175
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