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| Section | Weight | Objectives |
|---|---|---|
| AI Fundamentals | 16% | - AI capabilities and limitations - Concepts and terminology of Artificial Intelligence - Types of AI and Machine Learning |
| Trustworthy AI | 9% | - Ethical considerations and bias - Privacy and security - Transparency and explainability |
| CPMAI Methodology | 41% | - Phase I: Problem Identification
|
| Data for AI | 13% | - DataOps concepts - Data preparation and preprocessing - Data strategy and governance |
| Machine Learning | 13% | - Supervised, Unsupervised, and Reinforcement Learning - Algorithms and models (e.g., NLP, Computer Vision) - Deep Learning and Neural Networks |
| Managing AI | 8% | - Managing AI project teams and resources - Stakeholder management - Risk management in AI projects |
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NEW QUESTION # 249
A government agency plans to increase personalization of their AI public services platform. The agency is concerned that the personal information may be hacked. Which action should occur to achieve the agency's goals?
Answer: B
Explanation:
Enhancing data privacy directly addresses the risk of personal information being compromised while supporting personalization. Strong privacy protections increase user trust and confidence in the AI public services platform.
NEW QUESTION # 250
A healthcare organization is evaluating a diagnostic screening model. Clinical stakeholders state that missing a positive case has far more serious consequences than a false alarm.
Which evaluation metric should be prioritized?
Answer: A
Explanation:
Recall measures the proportion of actual positive cases the model correctly identifies, so maximizing it minimizes false negatives. When a missed positive carries severe consequences, recall is prioritized even at the cost of additional false positives.
NEW QUESTION # 251
An aerospace company is in the data preparation phase of an AI project. The project team must verify data quality to make a go/no-go decision for model development. They need to integrate data from several sensors with different sampling rates. What is an effective method that helps to ensure data consistency?
Answer: A
Explanation:
Utilizing data interpolation methods helps align sensor data collected at different sampling rates by estimating values at common time intervals. This improves consistency across sensor streams before making the go/no-go decision for model development.
NEW QUESTION # 252
An AI initiative has demonstrated a technically successful model, but the business unit cannot quantify what the improvement is worth.
What was most likely omitted during Business Understanding?
Answer: B
Explanation:
Business value is calculated as the difference between the AI-enabled outcome and the existing baseline. Without measuring current process performance before the project, improvements cannot be quantified or attributed to the AI solution.
NEW QUESTION # 253
A team is getting ready to begin working on a ML project. They need to build a data preparation pipeline and someone on the team suggests they reuse the same pipeline they created for their last project. What's wrong with this suggestion?
Answer: B
Explanation:
Data preparation pipelines must be tailored to the specific pattern and model needs of each project, so reusing pipelines without customization can lead to suboptimal results.
NEW QUESTION # 254
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