A transportation company is preparing data for an AI model to optimize fleet management. The project team is working with large amounts of structured and unstructured data.
If the project manager avoids addressing the variety of data during preparation, what will be the result?

• A.Improved model accuracy
• B.Increased data consistency
• C.Decreased data processing speed
• D.Reduced model performance

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An organization's leadership team is concerned about the ethical implications of operationalizing their AI model. How should the project manager address these concerns in their presentation to the team?

• A.Highlight the model's high performance metrics and low error rates
• B.Discuss the implementation of differential privacy and the algorithms used to protect data
• C.Demonstrate the use of bias detection tools to ensure fairness
• D.Explain how the AI model complies with general data protection regulation (GDPR) and other regulations

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A company is evaluating whether to implement AI for a project. They have defined their business objectives and determined the AI capability they want to use.
Which action will enable the project manager to move forward with the project?

• A.Implementing a preliminary version of the AI solution
• B.Identifying the contingency procedures
• C.Conducting a go/no-go assessment
• D.Conducting a data quality assessment

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A manufacturing company is operationalizing an AI-driven quality control system. The project manager needs to ensure data privacy and regulatory compliance due to the critical nature of protecting sensitive operational data.
What is an effective technique that addresses these requirements?

• A.Implementing a zero-trust architecture for network security
• B.Utilizing a secure multiparty computation framework
• C.Applying data anonymization to the dataset
• D.Using a hybrid encryption scheme for storage

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A city transportation department is deploying an AI model that adjusts traffic signal timing. The department is concerned that traffic patterns will shift seasonally and during major events. What is the best method to manage this risk after deployment?

• A.Perform continuous monitoring and auditing for drift and performance degradation
• B.Increase the training dataset size once before launch
• C.Disable model updates to maintain consistent behavior
• D.Rely on vendor guarantees instead of internal controls

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