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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Identify Business Needs and Solutions | 26% | - Align AI initiatives with organizational strategy - Evaluate feasibility and value of AI solutions - Define requirements, scope, and success criteria |
| Topic 2: Manage AI Model Development and Evaluation | 16% | - Address model drift, explainability, and limitations - Oversee model design, training, and validation - Monitor performance, accuracy, and reliability |
| Topic 3: Identify Data Needs | 26% | - Plan data collection, storage, and infrastructure - Define data requirements and sources - Ensure data quality, privacy, security, and compliance |
| Topic 4: Support Responsible and Trustworthy AI Efforts | 15% | - Ensure fairness, transparency, accountability - Manage bias, risk, compliance, and societal impact - Establish ethical and governance frameworks |
| Topic 5: Operationalize AI Solution | 17% | - Deploy AI systems into production - Establish monitoring, maintenance, and improvement processes - Manage change, adoption, and governance post-launch |
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NEW QUESTION # 232
An inexperienced team is training a neural network model on a desktop computer and this is taking a significant amount of time. What would you recommend to them to speed up model training?
Answer: D
NEW QUESTION # 233
During which phase of an AI project should you consider Trustworthy AI considerations?
Answer: A
Explanation:
Trustworthy AI principles must be integrated throughout every phase of the AI project to ensure ethical, transparent, and responsible AI development and deployment.
NEW QUESTION # 234
You're creating an AI enabled chatbot that is going to access user data. What areas related to data governance do you need to make sure you're addressing? (Choose all that apply.)
Answer: A,B,C,D,E
Explanation:
Data governance for an AI chatbot must address data sharing challenges, privacy risks, security risks, data quality issues, and associated business risks to ensure responsible and compliant data use.
NEW QUESTION # 235
A national health insurance company is embarking on a complex AI project to assist in coordinating patient care across its multiple hospital network. The AI system will analyze large amounts of patient data to coordinate care, improve patient outcomes, and optimize resource allocation. Numerous healthcare providers' data needs to be integrated. The data includes patient information which is private. The project needs to comply with data privacy regulations in various countries. Which critical step should be performed to optimize representative training data?
Answer: D
Explanation:
Improving data understanding and preparation is critical for optimizing representative training data because the team must assess data sources, quality, completeness, diversity, privacy constraints, and integration issues before training the AI system. This helps ensure patient data from multiple providers is usable, compliant, and representative across the hospital network.
NEW QUESTION # 236
Using machine learning and other cognitive approaches to understand how to take past / existing behavior and predict future outcomes or help humans make decisions about future outcomes using insight learned from past behavior / interactions / data is a core part to which pattern(s) of AI?
Answer: D
Explanation:
Using machine learning to analyze past behavior and predict future outcomes or assist decision- making is central to the Predictive Analytics & Decision Support pattern in AI.
NEW QUESTION # 237
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