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| Section | Weight | Objectives |
|---|---|---|
| Machine Learning | 13% | - Deep Learning and Neural Networks - Supervised, Unsupervised, and Reinforcement Learning - Algorithms and models (e.g., NLP, Computer Vision) |
| Managing AI | 8% | - Stakeholder management - Managing AI project teams and resources - Risk management in AI projects |
| AI Fundamentals | 16% | - Concepts and terminology of Artificial Intelligence - Types of AI and Machine Learning - AI capabilities and limitations |
| Data for AI | 13% | - Data strategy and governance - DataOps concepts - Data preparation and preprocessing |
| Trustworthy AI | 9% | - Ethical considerations and bias - Privacy and security - Transparency and explainability |
| CPMAI Methodology | 41% | - Phase V: Operationalization
|
1. A telecommunications company is considering an AI solution to improve customer service through automated chatbots. The project team is assessing the feasibility of the AI solution by examining its potential scalability and effectiveness. What will present the highest risk to the company?
A) The chatbot may not integrate well with existing customer service platforms.
B) The team may lack experience implementing AI-based customer service solutions.
C) The solution may not handle the volume of customer queries effectively.
D) The solution might breach customer data privacy regulations, leading to legal consequences.
2. A healthcare organization is evaluating the operationalization of an AI-powered diagnostic tool.
The tool is expected to enhance diagnostic accuracy and speed, thereby reducing patient wait times. The project manager needs to prepare a business case to present to the board of directors. Which method will help to ensure a robust business case?
A) Conducting a stakeholder impact analysis
B) Developing a predictive analytics model
C) Drafting a detailed implementation roadmap
D) Performing a cost-effectiveness study
3. Senior management has tasked your group to analyze a data set to uncover insights into the data. What is the best approach to use to do this?
A) Data Governance
B) Data Integration
C) Data Mining or Data Analytics
D) Data preparation
4. 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 encounter compatibility issues with existing systems.
C) The custom project development time can increase due to adjustments.
D) The team can see a reduction in the overall project timeline.
5. A financial services firm is building an AI model to detect fraudulent transactions. Identifying and validating data sources is critical to the model's success. What is an effective method that helps to ensure data accuracy?
A) Employing a federated database system for decentralized data access
B) Utilizing data lineage tools to track data origin and transformations
C) Implementing a blockchain-based ledger for transaction data
D) Setting up a batch processing system for data cleansing
Solutions:
| Question # 1 Answer: D | Question # 2 Answer: D | Question # 3 Answer: C | Question # 4 Answer: D | Question # 5 Answer: B |
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