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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: AI System Testing and Evaluation | - Model evaluation and monitoring
| |
| Topic 2: Data Preparation for AI | - Data cleaning and transformation
| |
| Topic 3: AI Model Development and Iteration | - Model building and validation
| |
| Topic 4: AI Operationalization and Governance | - Deployment and lifecycle management
| |
| Topic 5: Identify Business Needs and Solutions | 26% | - Problem framing and business alignment
|
| Topic 6: Data for AI | - Data identification and governance
|
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NEW QUESTION # 209
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 # 210
A project team is using a prompt engineering approach to improve AI/machine learning (ML) model outputs. They started with broad questions and then narrowed down the specific elements.
If the team had provided insufficient context, what would be the result?
Answer: A
Explanation:
Insufficient context prevents the model from understanding the intended goal, constraints, audience, or required details. As a result, the generated responses are likely to be vague, misaligned, or not relevant to the specific task.
NEW QUESTION # 211
A project team is working on an AI solution that needs to be both scalable and compliant with various regulations. They need to ensure all cognitive components are thoroughly evaluated for ethical considerations. The team has identified multiple patterns for the project.
How should the team proceed?
Answer: C
Explanation:
A detailed ethical review ensures that every cognitive component is assessed for issues such as fairness, transparency, accountability, privacy, and regulatory implications. This allows the team to identify and address ethical risks before the AI solution is scaled or deployed.
NEW QUESTION # 212
A project manager is evaluating an AI model's performance to determine its business impact. The model has shown high accuracy during testing but has not been implemented yet in a live environment. The project manager needs to decide what to assess to ensure the model's effectiveness. What should the project manager do?
Answer: A
Explanation:
Checking whether the training data represents real-world scenarios helps determine if the model's high testing accuracy is likely to translate into effective performance after deployment. A model can test well but fail to deliver business impact if the data used for training and testing does not reflect live operating conditions.
NEW QUESTION # 213
A company plans to operationalize an AI solution. The project manager needs to ensure model performance is meeting selected thresholds before release. What is an effective way to confirm these thresholds before this release?
Answer: C
Explanation:
Testing against validation datasets confirms whether the model meets the selected performance thresholds before release. This provides objective evidence that the model performs reliably on data separate from training data and is ready for operationalization.
NEW QUESTION # 214
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