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PMI CPMAI Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Trustworthy AI | 9% | - Privacy and security - Ethical considerations and bias - Transparency and explainability |
| Data for AI | 13% | - Data preparation and preprocessing - DataOps concepts - Data strategy and governance |
| Machine Learning | 13% | - Algorithms and models (e.g., NLP, Computer Vision) - Supervised, Unsupervised, and Reinforcement Learning - Deep Learning and Neural Networks |
| Managing AI | 8% | - Stakeholder management - Managing AI project teams and resources - Risk management in AI projects |
| CPMAI Methodology | 41% | - Phase VI: Iteration & Monitoring
|
| AI Fundamentals | 16% | - Types of AI and Machine Learning - AI capabilities and limitations - Concepts and terminology of Artificial Intelligence |
PMI Cognitive Project Management in AI (PMI-CPMAI) Sample Questions:
1. 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.)
A) Implementing a data normalization process
B) Applying a diversified data ingestion model
C) Depending only on manual entry for format standardization
D) Adopting a hybrid data preparation pipeline
E) Utilizing a multiformat data integration tool
2. In the finance sector, a company is implementing an AI system for credit risk assessment. The project manager needs to identify the data subject matter experts (SMEs) who can help to ensure the accuracy and reliability of the model. What is an effective method to achieve this objective?
A) Select SMEs based on their availability rather than expertise.
B) Rely on general IT staff for data and financial expertise.
C) Engage with internal data analysts and financial experts.
D) Focus on SMEs with experience in noncognitive solutions.
3. A project team is evaluating whether an AI initiative should proceed beyond discovery.
Stakeholders are aligned on objectives, but the team has not confirmed data access, quality, or legal constraints. What is the most appropriate next action?
A) Purchase additional compute infrastructure
B) Conduct a go/no-go assessment using readiness criteria
C) Move directly to deployment planning
D) Begin model development using sample data
4. You are working for a large multinational organization and have been assigned to a new project.
For your new ML project, you need to make sure you're managing data privacy and security as you're working with sensitive customer data. What critical security issues do you need to make sure you address? (Choose all that apply.)
A) Securing data at rest
B) Securely storing all data collected for training purposes
C) Compliance with Data Privacy Laws even if they are out of your physical jurisdiction
D) Securing model data and metadata
5. A project involves integrating AI systems across multiple departments, each with different access levels. This complex Al project has presented the project manager with significant issues related to data misuse. The project team has been focused on their ethics guidelines but continues to experience data misuse. The project involves different regional data protection regulations which further increases the complexity. What issue will cause these challenges to occur?
A) Overlooking algorithmic bias and fairness concerns
B) Failure to implement robust encryption for data security
C) Limited awareness of explainability requirements
D) Lack of a detailed plan addressing a governance strategy
Solutions:
| Question # 1 Answer: A,E | Question # 2 Answer: C | Question # 3 Answer: B | Question # 4 Answer: A,B,C,D | Question # 5 Answer: D |
