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

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A project team is evaluating whether an AI initiative should proceed beyond discovery. Stakeholders are aligned on objectives, but the team has not confirmed data access, quality, or legal constraints. What is the most appropriate next action?

• A.Begin model development using sample data
• B.Conduct a go/no-go assessment using readiness criteria
• C.Move directly to deployment planning
• D.Purchase additional compute infrastructure

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A project manager is tasked with overseeing the implementation of an AI model for financial forecasting.
They need to ensure the model's predictions are reliable.
If the model's error rate exceeds acceptable boundaries, what will occur next?

• A.Operationalization delays due to model retraining
• B.Reduced need for human oversight since additional AI models will be used
• C.Higher than expected computational costs
• D.Increased stakeholder confidence that the project team will correct

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A project manager is tasked with ensuring that an AI project complies with data regulations before data collection begins. This involves identifying all necessary requirements for trustworthy AI, including ethical considerations, privacy, and transparency.
What should the project manager do first?

• A.Draft a detailed data governance framework to be reviewed later.
• B.Perform a comprehensive assessment of data regulations and compliance requirements.
• C.Schedule a meeting with stakeholders to discuss potential data collection compliance issues.
• D.Develop a high-level strategy for data collection and aggregation.

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A manufacturing firm is planning to implement a network of intelligent machines to increase efficiency on the assembly line. The machines are equipped with advanced AI capabilities including precision assembly, quality control for predictive maintenance, and real-time data analysis. The intelligent machines should enhance operational efficiency, reduce downtime, and improve product quality. There needs to be seamless communication between the machines and existing systems, compliance with industry regulations, and a managed transition for the workforce.
What is a beneficial outcome of using intelligent machines in this environment?

• A.Scalability and flexibility in production
• B.Over-reliance on technology leading to skill degradation
• C.Higher investment costs without immediate returns
• D.Increased vulnerability to cybersecurity threats

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