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| Section | Weight | Objectives |
|---|---|---|
| AI Operationalization and Governance | - Deployment and lifecycle management
| |
| Data Preparation for AI | - Data cleaning and transformation
| |
| AI Model Development and Iteration | - Model building and validation
| |
| Identify Business Needs and Solutions | 26% | - Problem framing and business alignment
|
| AI System Testing and Evaluation | - Model evaluation and monitoring
| |
| Data for AI | - Data identification and governance
|
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NEW QUESTION # 43
A project manager is overseeing the development of an AI system and is concerned about maintaining user privacy. The project involves collecting sensitive user data with potential legal and compliance issues to consider.
What should the project manager prioritize to help ensure the AI system adheres to privacy requirements?
Answer: C
Explanation:
Regular privacy impact assessments help identify how sensitive data is collected, processed, stored, and used, while evaluating associated privacy risks against applicable legal and regulatory requirements. This enables the project team to maintain ongoing compliance throughout the AI system's lifecycle.
NEW QUESTION # 44
An AI project team needs to assess the feasibility of an AI solution. In order to proceed with the AI project, what should the project manager address?
Answer: D
Explanation:
Alignment with business objectives is essential for determining whether the AI solution is feasible and worth pursuing. The project should proceed only when the AI approach supports the intended business outcomes, success criteria, and organizational value.
NEW QUESTION # 45
A financial services firm is assessing the success of a newly operationalized AI system for fraud detection. The project manager needs to evaluate the model against business key performance indicators. (KPIs). What is an effective method to help ensure the accuracy of this evaluation?
Answer: C
Explanation:
Utilizing a diverse set of validation techniques improves evaluation accuracy by checking the AI fraud detection system from multiple perspectives, such as model performance, business KPI alignment, false positive and false negative rates, and operational impact. This gives a more reliable assessment than relying on one metric or broad financial reporting alone.
NEW QUESTION # 46
During which phase of an AI project should you consider Trustworthy AI considerations?
Answer: B
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 # 47
An IT services company is verifying data quality for an AI project aimed at predicting server downtimes. The project manager needs to decide whether to proceed with data preparation.
Which technique should the project manager use?
Answer: A
Explanation:
Exploratory data analysis helps verify whether the server downtime data is complete, accurate, consistent, relevant, and suitable before moving into data preparation. It allows the project manager to identify missing values, outliers, patterns, anomalies, and quality issues that could affect model development.
NEW QUESTION # 48
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