AI Use Case Identification and Value Prioritization CAIPM Practice Questions (Page 2)

Questions 6–10

1. 6expert · hard

   A social media company is considering an AI system to detect hate speech. The system would use user-generated content, which raises concerns about freedom of speech and potential bias. The company's legal team warns about regulatory fines, while the public relations team is concerned about brand reputation. The system has high strategic alignment with the company's safety goals. What should the AI program manager do?

   • AProceed with the use case but include a robust ethical review and transparency measures
   • BProceed immediately because the strategic alignment justifies the risks
   • CReject the use case because of the risk of bias and censorship
   • DDelay the decision until public opinion is surveyed
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2. 7foundation · easy

   Which criterion is used to evaluate whether an AI use case supports the organization's long-term objectives?

   • AFeasibility
   • BRisk
   • CROI
   • DStrategic alignment
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4. 8foundation · easy

   During a workshop to identify AI use cases, a project manager asks the team to list all the places where employees spend significant time on repetitive manual tasks. Which activity is the team performing?

   • AAssessing the organization's data readiness
   • BDefining the AI governance framework
   • CPrioritizing use cases using a scoring model
   • DIdentifying potential AI use cases by analyzing pain points
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5. 9application · medium

   A retail company wants to implement an AI-based demand forecasting system. The data science team is skilled in building models, but the company's historical sales data is stored across three different legacy databases with inconsistent product codes and missing promotion records. The program manager must assess feasibility. What is the most critical resource gap to address before prioritizing this use case?

   • ATechnology infrastructure, because the legacy databases cannot support any modern AI workload.
   • BTalent shortage, because the data science team lacks the specific skills needed for forecasting.
   • CData quality and integration, because the model's accuracy depends on clean, unified historical data.
   • DExecutive sponsorship, because without a champion the project will not receive funding.
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6. 10expert · hard

   An e-commerce company is evaluating an AI-powered dynamic pricing model. The company has extensive historical transaction data, but the data is stored in a data lake with no data quality checks. The company has a small data science team that is already overcommitted. The marketing team is enthusiastic about the revenue potential, but the customer service team is concerned about customer backlash. The company's leadership wants to implement the model within three months. What is the most critical feasibility constraint?

   • ATechnology infrastructure, because the data lake cannot support real-time pricing updates.
   • BTalent availability, because the data science team is overcommitted and cannot dedicate time to this project.
   • CCustomer backlash, because negative customer reactions could damage the brand and reduce long-term revenue.
   • DData quality, because the data lake's lack of quality checks means the model may be trained on inaccurate or incomplete data, undermining its reliability.
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