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| Section | Weight | Objectives |
|---|---|---|
| Operationalize AI Solution | 17% | - Manage change, adoption, and governance post-launch - Deploy AI systems into production - Establish monitoring, maintenance, and improvement processes |
| 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 |
| Identify Data Needs | 26% | - Ensure data quality, privacy, security, and compliance - Define data requirements and sources - Plan data collection, storage, and infrastructure |
| Manage AI Model Development and Evaluation | 16% | - Monitor performance, accuracy, and reliability - Oversee model design, training, and validation - Address model drift, explainability, and limitations |
| Support Responsible and Trustworthy AI Efforts | 15% | - Ensure fairness, transparency, accountability - Manage bias, risk, compliance, and societal impact - Establish ethical and governance frameworks |
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NEW QUESTION # 126
A project team is working on an AI initiative that requires ensuring the explainability and transparency of their selected algorithms. They have identified the business requirements and stakeholders. What is an effective way to address the project objectives?
Answer: D
Explanation:
Using a rule-based approach supports explainability and transparency because the decision logic is explicit, traceable, and easier for stakeholders to understand. This makes it suitable when the project requires clear reasoning behind algorithmic outputs.
NEW QUESTION # 127
A project manager is preparing a final report on an AI project. The report must highlight lessons learned, focusing on ethical concerns and compliance with data regulations. In addition, the team has identified multiple ethical issues related to data privacy during the project. What is an effective approach to address the situation for future AI projects?
Answer: B
Explanation:
Implementing a robust ethical data governance framework provides a structured way to manage privacy, compliance, accountability, consent, data usage, and ethical risks across future AI projects. This directly addresses lessons learned from multiple data privacy issues and helps prevent recurrence.
NEW QUESTION # 128
Your organization wants to use Generative AI. What are examples of when Generative AI can and should be used? (Choose all that apply.)
Answer: A,B,D,E,F
Explanation:
Generative AI is well-suited for augmenting human tasks, creating content, augmenting training data, automating content generation programmatically, and powering virtual avatars. Explainable decision-support typically involves different AI techniques focused on transparency rather than generation.
NEW QUESTION # 129
A project team at an IT services company is developing an AI solution to enhance network security. They need to define the success criteria to help ensure the project achieves its desired outcomes. What should the project manager do to define the relevant success criteria?
Answer: C
Explanation:
Key performance indicators for incident response times and threat detection rates provide measurable success criteria tied directly to the network security objectives. They allow the project team to evaluate whether the AI solution improves detection effectiveness and response efficiency.
NEW QUESTION # 130
In what way would you be using Generative AI if you used the results of the Generative AI solution to improve and accelerate your job?
Answer: B
NEW QUESTION # 131
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