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A government agency is operationalizing a new AI tool for predictive policing. The project manager needs to identify data subject matter experts (SMEs) to ensure data quality and relevance. The project team has access to historical crime data, socioeconomic data, and real-time incident reports.
Which method will help in determining the data SMEs for this project?

• A.Reviewing certifications in advanced data analytics and machine learning
• B.Conducting workshops to assess knowledge in real-time incident data processing
• C.Identifying individuals who have worked on similar AI tools in policing
• D.Evaluating the team's familiarity with historical crime and socioeconomic data

In a complex healthcare project, a provider plans to implement AI for patient data analysis to improve diagnostic accuracy. The project involves the need for interoperability between the AI systems and existing healthcare databases. These databases contain sensitive patient information. The requirements involve strict ethical and legal regulations in various countries.
Which critical step must be performed?

• A.Maintaining high prediction accuracy
• B.Implementing privacy impact assessments
• C.Creating a regulatory impact report
• D.Performing a detailed financial risk analysis

A consulting firm is preparing data for an AI-driven customer segmentation model. They need to verify data quality before data preparation.
What should the project manager do first?

• A.Conduct data cleaning.
• B.Assess data completeness.
• C.Apply data labeling techniques.
• D.Implement data enhancement.

A financial institution is implementing a new AI system for fraud detection. The project team must ensure the data meets the needs of the AI solution by verifying data quality, completeness, and relevance. They have access to various internal and external data sources.
Which method addresses the project team's objectives?

• A.Using pretrained models without tailoring to specific data
• B.Conducting a comprehensive data audit and cleansing process
• C.Limiting the data sources to internal databases to avoid complications
• D.Integrating data without improvement checks to expedite the project timeline

During the configuration management of an AI/machine learning (ML) model, the team has observed inconsistent performance metrics across different test datasets.
What will cause the inconsistency issue?

• A.Overfitting the training data
• B.Low variance in the test results
• C.Insufficient model complexity
• D.Incorrect data preprocessing steps