In an IT services firm, the AI project team is tasked with developing a virtual assistant to support customer service operations. The assistant must integrate seamlessly with existing customer relationship management (CRM) systems and handle a variety of customer queries.
Which necessary initial task should the project manager take?

• A.Building a dedicated data lake
• B.Conducting a comprehensive data audit
• C.Designing a custom AI algorithm that enhances the chatbot ' s capacity
• D.Procuring advanced natural language processing (NLP) libraries

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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?

• A.Data augmentation strategies
• B.Advanced data labeling methods
• C.Detailed cost-benefit analysis
• D.Exploratory data analysis (EDA)

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An AI project team needs to consider compliance with data regulations and explainability standards as requirements for a new AI solution.
At what point in the project should the requirements be approached?

• A.As part of the data preparation phase
• B.As part of the business understanding phase
• C.As part of the final testing phase
• D.As optional guidelines based on project scope

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A government agency is planning to implement a new AI-driven public service system. The project manager needs to develop a business case to secure funding. The agency's goals are to improve service delivery and reduce response times.
Which method will provide the results that meet the project manager's objective?

• A.Analyzing case studies from other agencies
• B.Creating a detailed ROI projection
• C.Holding stakeholder workshops
• D.Conducting a pilot program

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An AI project for a financial technology client is at risk due to potential inaccuracies in data aggregation.
What is the first step the project manager should take to mitigate the risk?

• A.Evaluate the data freshness and relevance
• B.Delete the suspicious data manually
• C.Understand the data characteristics
• D.Create a data visualization

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