A government agency is using an AI system to analyze public data for policymaking decisions.
The project manager needs to address the risks related to data accuracy, privacy, and misuse.
What represents the highest risk to the agency?

• A.The AI system relies on third-party providers.
• B.The user data is stored in an unsecured database.
• C.The system lacks a transparency process.
• D.The AI system is not regularly updated with new data.

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An IT services company project manager is creating an AI project scope statement. They need to include details on the environments, devices, and personnel that will use the AI solution. What should the project manager do?

• A.Gain stakeholder buy-in to proceed with the project.
• B.Develop a comprehensive usage scenario analysis.
• C.Perform a detailed technical requirements audit for the scope statement.
• D.Create an AI efficacy program to complete the scope statement.

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You're running an AI project and want to speed up training of the model so that you can complete your current CPMAI iteration within the two week timeframe the team has set.
What's one approach to speed up model training?

• A.Use cloud-based technology
• B.Use or extend a pre-trained model
• C.Use data from a previous project
• D.Use Brute force method

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A healthcare organization plans to use an AI solution to predict patient readmissions. The data science team needs to identify data sources and ensure data quality. Which method will meet the project team's objectives?

• A.Setting up a continuous integration pipeline for real-time data validation
• B.Using data profiling tools to assess data completeness
• C.Implementing data augmentation techniques to fill missing values
• D.Operationalizing a data catalog to maintain metadata standards

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The project team at an IT services company is working on an AI-based customer support chatbot.
To help ensure the chatbot functions effectively, they need to define the required data. Which method meets the project requirements?

• A.Developing a new script based on anticipated customer queries
• B.Gathering historical customer interaction logs for training data
• C.Integrating feedback from beta customers to refine the model
• D.Using synthetic data generated from sample customer conversations

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