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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Trustworthy AI | 9% | - Privacy and security - Transparency and explainability - Ethical considerations and bias |
| Topic 2: AI Fundamentals | 16% | - AI capabilities and limitations - Concepts and terminology of Artificial Intelligence - Types of AI and Machine Learning |
| Topic 3: Data for AI | 13% | - Data strategy and governance - DataOps concepts - Data preparation and preprocessing |
| Topic 4: CPMAI Methodology | 41% | - Phase II: Data Identification & Curation
|
| Topic 5: Machine Learning | 13% | - Algorithms and models (e.g., NLP, Computer Vision) - Supervised, Unsupervised, and Reinforcement Learning - Deep Learning and Neural Networks |
| Topic 6: Managing AI | 8% | - Managing AI project teams and resources - Risk management in AI projects - Stakeholder management |
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NEW QUESTION # 226
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: C
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 # 227
A healthcare provider is operationalizing an AI tool to assist in diagnostic processes. To ensure robust model governance, they need to address data privacy and ethical considerations. What should the project manager do?
Answer: D
Explanation:
Developing a detailed privacy impact assessment helps identify, evaluate, and mitigate privacy and ethical risks before and during operationalization. For a healthcare diagnostic AI tool, this supports responsible data handling, regulatory compliance, patient protection, and strong model governance.
NEW QUESTION # 228
An aerospace company is operationalizing an AI system to optimize flight routes. The system must be safe and comply with aviation regulations. The project team is evaluating methods to prevent potential safety risks.
Which method should the project manager use to prevent safety risk issues?
Answer: B
Explanation:
Extensive simulation testing exposes the AI system to a wide range of normal, abnormal, and edge-case flight conditions before operational use. This helps identify unsafe behaviors and failure modes early, allowing the team to address safety risks before they affect real-world flight operations.
NEW QUESTION # 229
Upper management is looking to roll out a new product and wants to see if there are any patterns and insights that can be discovered from customer data. Your team has been tasked to discover these potential patterns and structures within this data.
Which type of machine learning approach would be most appropriate to pick for this problem?
Answer: B
NEW QUESTION # 230
A project member is deciding on the appropriate AI algorithm for a new project. The goal is to achieve high accuracy while maintaining transparency in the decision-making process.
What should the project manager suggest?
Answer: A
Explanation:
Evaluating algorithm explainability helps ensure the selected model can provide understandable reasons for its predictions or decisions while still meeting performance requirements. This directly supports the project's need to balance accuracy with transparency.
NEW QUESTION # 231
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