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Last Updated: Jul 22, 2026
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| Section | Weight | Objectives |
|---|---|---|
| Data for AI | 13% | - Data preparation and preprocessing - DataOps concepts - Data strategy and governance |
| CPMAI Methodology | 41% | - Phase I: Problem Identification
|
| AI Fundamentals | 16% | - Concepts and terminology of Artificial Intelligence - Types of AI and Machine Learning - AI capabilities and limitations |
| Managing AI | 8% | - Stakeholder management - Managing AI project teams and resources - Risk management in AI projects |
| Trustworthy AI | 9% | - Ethical considerations and bias - Privacy and security - Transparency and explainability |
| Machine Learning | 13% | - Supervised, Unsupervised, and Reinforcement Learning - Deep Learning and Neural Networks - Algorithms and models (e.g., NLP, Computer Vision) |
1. A team has started working on their first AI project and they are running this project like a traditional software development project. About two months into the project the team is hitting some major issues, and you're tasked with coming in to help manage this project. Immediately you realize that AI projects need to be treated like data centric projects. What's the next best course of action?
A) Hire an entirely new team making sure there is at least one data scientist on this centrteam
B) Hire an outside consulting firm to handle the technical aspects while you train the team yourselfon data centric best practices
C) Bring in data centric methodology best practices to get this project back on track
D) Get the existing team up to speed and make sure existing Agile approaches can support the AI effort
2. A company needs to launch an AI application quickly to be the first to the market. The project team has decided to use pretrained models for their current AI project iteration. What is a key result of leveraging pretrained models?
A) The project can face unexpected scalability challenges.
B) The team can see a reduction in the overall project timeline.
C) The custom project development time can increase due to adjustments.
D) The team can encounter compatibility issues with existing systems.
3. A project manager at an IT services company needs to present a business case for adopting an AI-driven cost-benefit analysis tool. The objective is to demonstrate the tool's value compared to existing processes. Which method will meet the project manager's objective?
A) Developing a proof of concept to highlight efficiency gains
B) Using a decision tree to simulate various cost scenarios
C) Creating a detailed heuristic-based comparison report
D) Implementing a fuzzy logic system for preliminary evaluations
4. A project team is working on an AI initiative that requires ensuring the explainability and transparency of their selected algorithms. They have identified the business requirements and stakeholders. What is an effective way to address the project objectives?
A) Using a rule-based approach to maintain simplicity
B) Adopting a principle of least privilege for data access
C) Leveraging generative AI for advanced data analysis
D) Implementing a chain-of-thought prompting technique
5. An AI project team is assessing the scalability of a healthcare solution. Which factor should the project manager consider to help ensure the solution is scalable?
A) Integration with the existing infrastructure
B) Human oversight requirements
C) Ability to handle increased loads
D) Compliance with data regulations
Solutions:
| Question # 1 Answer: C | Question # 2 Answer: B | Question # 3 Answer: A | Question # 4 Answer: A | Question # 5 Answer: C |
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