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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Data for AI | 13% | - Data strategy and governance - DataOps concepts - Data preparation and preprocessing |
| Topic 2: CPMAI Methodology | 41% | - Phase V: Operationalization
|
| Topic 3: Managing AI | 8% | - Managing AI project teams and resources - Stakeholder management - Risk management in AI projects |
| Topic 4: Machine Learning | 13% | - Deep Learning and Neural Networks - Algorithms and models (e.g., NLP, Computer Vision) - Supervised, Unsupervised, and Reinforcement Learning |
| Topic 5: Trustworthy AI | 9% | - Privacy and security - Transparency and explainability - Ethical considerations and bias |
| Topic 6: AI Fundamentals | 16% | - AI capabilities and limitations - Types of AI and Machine Learning - Concepts and terminology of Artificial Intelligence |
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NEW QUESTION # 135
One of the key elements of a data-centric methodology is the data requirements phase. During CPMAI Phase II, several unexpected issues have developed and are now threatening the data collection efforts. What course of action might make the issue worse?
Answer: A
Explanation:
Expanding the scope when facing data collection issues can worsen problems by increasing complexity and resource demands, making it harder to manage and deliver the project successfully.
NEW QUESTION # 136
Your team is working on a project and is running into some issues. You need someone on the team who is able to solve problems in environments of uncertainty, can deal with failure, and has the math and data visualization skills needed to communicate the results with others so the issues can get resolved.
What role would be most suited for this?
Answer: B
NEW QUESTION # 137
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: C
NEW QUESTION # 138
Your organization has just rolled out a new image recognition system and is asking all employees to use it. It was trained using images from the ImageNet test set. After a few weeks, users are finding the results are not as expected and are asking for visibility into all the aspects of what went into building an AI system. What area of Trustworthy AI is being addressed here?
Answer: B
Explanation:
Transparent AI focuses on providing visibility into how AI systems are built, including data sources and processes, to build user trust and understanding.
NEW QUESTION # 139
A consulting firm is preparing data for an AI-driven customer segmentation model. They need to verify data quality before data preparation. What should the project manager do first?
Answer: A
Explanation:
Assessing data completeness should be done first because the team must verify whether the customer data contains the required fields, records, and coverage before cleaning, enhancing, labeling, or preparing it for the segmentation model.
NEW QUESTION # 140
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