A project team is trying to determine the most suitable environment to operationalize their AI/machine learning (ML) solution. They need to consider various factors to help ensure a successful implementation.
What should the project manager do?

• A. Consider the cost of implementation
• B. Evaluate the system ' s scalability options
• C. Identify the end users and their interactions
• D. Analyze the solution ' s compliance requirements

A government project plans to implement an AI-based fraud detection system and the project team needs to define the success criteria. They identified potential improvements in detection accuracy, reduction in investigation time, and cost savings as key performance indicators (KPIs). However, they are unsure how to effectively quantify these KPIs.
Which two approaches should be used? (Choose 2)

• A. Use random benchmarks without industry comparison
• B. Rely on only qualitative feedback from stakeholders
• C. Establish a baseline using historical data comparisons
• D. Set fixed performance targets based on theoretical models
• E. Implement a continuous performance monitoring system

An AI project team in the healthcare sector is tasked with developing a predictive model for patient readmissions. They need to gather required data from various sources, including electronic health records (EHR), patient surveys, and clinical notes. The team is evaluating which technique will help to ensure the data is comprehensive and reliable.
What is an effective technique the project team should use?

• A. Employing natural language processing (NLP) to extract relevant data from clinical notes
• B. Using federated learning to train models across decentralized data sources without centralizing data
• C. Utilizing real-time data integration from EHR systems to ensure data freshness
• D. Implementing data augmentation techniques to enhance dataset diversity

A fintech AI project uses third-party data sources for credit risk modeling. The project manager is concerned about compliance and accountability if the external data quality changes. Which control best supports responsible and trustworthy AI delivery?

• A. Remove all external data sources immediately
• B. Only document model performance once at launch
• C. Establish data governance and supplier controls, including auditability and monitoring
• D. Allow each team to apply its own data definitions

A telecommunications company ' s AI project team is operationalizing a predictive maintenance model for network equipment. They need to meticulously manage the model ' s configuration to avoid potential failures.
Which method will help the model configuration remain consistent and avoid drift?

• A. Implementing automated retraining schedules
• B. Performing regular manual inspections
• C. Employing frequent algorithm operationalizations
• D. Utilizing version control systems