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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Manage AI Model Development and Evaluation | 16% | - Oversee model design, training, and validation - Monitor performance, accuracy, and reliability - Address model drift, explainability, and limitations |
| Topic 2: Operationalize AI Solution | 17% | - Establish monitoring, maintenance, and improvement processes - Deploy AI systems into production - Manage change, adoption, and governance post-launch |
| Topic 3: Identify Business Needs and Solutions | 26% | - Define requirements, scope, and success criteria - Align AI initiatives with organizational strategy - Evaluate feasibility and value of AI solutions |
| Topic 4: Identify Data Needs | 26% | - Ensure data quality, privacy, security, and compliance - Define data requirements and sources - Plan data collection, storage, and infrastructure |
| Topic 5: Support Responsible and Trustworthy AI Efforts | 15% | - Manage bias, risk, compliance, and societal impact - Establish ethical and governance frameworks - Ensure fairness, transparency, accountability |
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NEW QUESTION # 304
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 # 305
A government health agency is considering the implementation of AI to predict disease outbreaks. The project manager needs to determine whether AI is the right solution to address their needs. The solution must analyze large datasets from various regions and provide accurate predictions without overwhelming the system. In addition, the agency has non-Al alternatives such as traditional statistical models. What is a necessary initial task the project manager should perform?
Answer: B
Explanation:
Identifying which parts of the problem require cognitive solutions is the necessary initial task because the project manager must first determine whether AI is actually needed or whether traditional statistical or non-AI methods can meet the agency's outbreak prediction needs.
NEW QUESTION # 306
A project manager must report iteration progress to an executive sponsor who has no technical background. The team's most recent results include improvements in F1 score and AUC.
How should the progress be communicated?
Answer: D
Explanation:
Executive stakeholders evaluate progress against business outcomes, not model diagnostics.
Mapping technical improvements to the success criteria defined in Business Understanding keeps reporting relevant and supports informed sponsorship decisions.
NEW QUESTION # 307
You are establishing the data requirements for the project. Which of the following tasks is the least likely to impact data requirements?
Answer: D
NEW QUESTION # 308
The confusion matrix measures how the algorithm performs for a binary classification activity. As your team is running tests to evaluate model performance, they are seeing the model is incorrectly categorizing flowers as trees. Your model is provided the following:
Answer: D
Explanation:
Incorrectly categorizing flowers as trees represents false positives - instances where the model incorrectly predicts the positive class when it should not.
NEW QUESTION # 309
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