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| Section | Weight | Objectives |
|---|---|---|
| CPMAI Methodology | 41% | - Phase III: Algorithm Selection & Model Training
|
| Managing AI | 8% | - Risk management in AI projects - Stakeholder management - Managing AI project teams and resources |
| Machine Learning | 13% | - Algorithms and models (e.g., NLP, Computer Vision) - Supervised, Unsupervised, and Reinforcement Learning - Deep Learning and Neural Networks |
| Trustworthy AI | 9% | - Ethical considerations and bias - Transparency and explainability - Privacy and security |
| AI Fundamentals | 16% | - Types of AI and Machine Learning - Concepts and terminology of Artificial Intelligence - AI capabilities and limitations |
| Data for AI | 13% | - Data strategy and governance - DataOps concepts - Data preparation and preprocessing |
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NEW QUESTION # 71
Major factors for the project you are currently working on is around the training time, cost, and complexity of training your models. Which algorithm is not the best choice given these constraints?
Answer: D
Explanation:
Neural networks typically require more training time, computational resources, and complexity compared to other algorithms, making them less suitable when training time, cost, and complexity are major constraints.
NEW QUESTION # 72
Your company is insisting on running an automation project and applying AI best practices and methodologies to the project. You understand that automating things is just the act of using machines to repeat tasks, and does not require AI to achieve results. You think it is overkill but the project moves forward as planned. What would likely have helped avoid this conflict?
Answer: B
Explanation:
Ensuring that all team members understand the fundamental differences between automation and AI systems helps align expectations and avoids conflicts about the appropriate use of AI best practices.
NEW QUESTION # 73
You have been brought on to manage a recognition project, specifically an image recognition project, for an Autonomous Retail application. You know that you need to make sure you have sufficient data for this project. What's the best way to approach this?
Answer: D
Explanation:
Taking inventory of all company data and selecting relevant data ensures you use appropriate, high-quality datasets tailored to the project needs without unnecessary or irrelevant data.
NEW QUESTION # 74
An AI project team has prepared the data and is ready to proceed with model development.
Which action should the project manager perform next?
Answer: B
Explanation:
Conducting a final assessment of data quality ensures the prepared data is complete, accurate, relevant, consistent, and ready for model development. This helps prevent poor-quality inputs from affecting training results and model reliability.
NEW QUESTION # 75
Creating machine learning models can be complicated. Your team wants to use tools called Automated Machine Learning (AutoML) to simplify the process. You know of another team that has used AutoML tools and it's saved the team a lot of time. However, what's the one area you should not have the AutoML tool help with?
Answer: B
NEW QUESTION # 76
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