AI Use Case Identification and Value Prioritization CAIPM Practice Questions (Page 8)

Questions 36–40

1. 36application · medium

   A government agency is considering an AI system to automate the review of visa applications. The system would use historical approval data, which may contain biases. During the prioritization process, what should the agency do?

   • ADefer the decision until a new AI ethics committee is formed
   • BProceed because the system will speed up processing and reduce backlogs
   • CConduct an ethical impact assessment and involve civil rights organizations in the review
   • DReject the use case because historical data is inherently biased
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2. 37application · medium

   A retail bank's AI program manager has prioritized a use case to automate credit-card limit increases using a machine learning model. The customer advocacy team is concerned about fairness and transparency, while the risk team is concerned about increased default rates. The marketing team is enthusiastic because it could increase card usage. What is the best way to incorporate these stakeholder concerns into the prioritization decision?

   • AEscalate to the CEO to resolve the conflict, since executive authority is needed to overrule the risk team.
   • BDefer the use case indefinitely, because any stakeholder concern is a reason to abandon the project.
   • CProceed with the use case as planned, because the marketing team's enthusiasm indicates strong business support.
   • DAdjust the use case's risk score to reflect the concerns and require a fairness audit and risk back-testing before final approval.
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4. 38foundation · easy

   A project manager places AI use cases on a grid with 'business value' on one axis and 'implementation complexity' on the other. Which prioritization framework is being used?

   • AValue/complexity matrix
   • BSWOT analysis
   • CMoSCoW method
   • DRACI matrix
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5. 39application · medium

   A logistics company has prioritized three AI use cases: (1) route optimization, (2) demand forecasting, and (3) warehouse robot scheduling. The route optimization model depends on accurate demand forecasts to be effective. The warehouse scheduling model depends on route data to coordinate pickup times. The data engineering team can only build one data pipeline at a time. What is the correct sequencing for the implementation roadmap?

   • ADemand forecasting first, then route optimization, then warehouse robot scheduling.
   • BAll three in parallel, because the data pipelines can be built independently.
   • CRoute optimization first, because it has the highest direct cost savings.
   • DWarehouse robot scheduling first, because it is the most complex and should be started early.
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6. 40expert · hard

   A financial services firm has prioritized three AI use cases: (1) anti-money-laundering (AML) transaction monitoring, (2) customer segmentation for marketing, and (3) algorithmic trading signals. The AML model has high regulatory value but requires significant data integration. The marketing segmentation model has moderate value and is easy to implement. The trading model has high profit potential but high regulatory risk and requires real-time infrastructure. The firm's compliance officer has mandated that any customer-facing or regulated model must undergo a formal review. The firm wants to show value within six months. What is the best roadmap approach?

   • AImplement all three in parallel to maximize learning and accelerate the AI program.
   • BImplement the trading model first because it has the highest profit potential and can fund the other initiatives.
   • CImplement AML monitoring first because it has the highest regulatory value and demonstrates responsible AI.
   • DImplement marketing segmentation first as a quick win, then tackle AML monitoring, and defer trading signals until the compliance review process is mature.
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