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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Data for AI | 13% | - DataOps concepts - Data preparation and preprocessing - Data strategy and governance |
| Topic 2: Machine Learning | 13% | - Supervised, Unsupervised, and Reinforcement Learning - Algorithms and models (e.g., NLP, Computer Vision) - Deep Learning and Neural Networks |
| Topic 3: Managing AI | 8% | - Stakeholder management - Managing AI project teams and resources - Risk management in AI projects |
| Topic 4: AI Fundamentals | 16% | - AI capabilities and limitations - Types of AI and Machine Learning - Concepts and terminology of Artificial Intelligence |
| Topic 5: Trustworthy AI | 9% | - Ethical considerations and bias - Transparency and explainability - Privacy and security |
| Topic 6: CPMAI Methodology | 41% | - Phase III: Algorithm Selection & Model Training
|
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NEW QUESTION # 207
Your team has created a model that is going to be used for monitoring systems and it needs to provide analysis on a weekly basis. What's the most appropriate Model Operationalization approach?
Answer: A
Explanation:
Batch prediction suits scenarios needing periodic analysis, like weekly monitoring, by processing accumulated data in scheduled batches.
NEW QUESTION # 208
A project team is tasked with ensuring all AI-related decisions and actions are documented comprehensively for future auditing purposes. They need to track the reasons for specific AI choices, their impacts, and any issues encountered during the implementation.
What is represented in this situation?
Answer: A
NEW QUESTION # 209
A project manager meets with a customer for initial discussions about an upcoming project. At the end of the meeting, the customer asks the project manager for a rough estimate of the project duration. Based on her experience with three similar projects, the project manager provides an estimate of 8-10 months. What's wrong with this timeframe?
Answer: A
Explanation:
The estimate likely overlooks the significant time required for data preparation, which is a critical and often time-consuming phase in AI projects.
NEW QUESTION # 210
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: A
NEW QUESTION # 211
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: B
NEW QUESTION # 212
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