Sustaining AI Transformation and Continuous Improvement CAIPM Practice Questions (Page 2)

Questions 6–10

1. 6foundation · easy

   What is a key element of applying DevOps principles to AI systems for continuous improvement?

   • AManual deployment of models to ensure careful review
   • BAutomating the deployment pipeline to enable frequent, reliable model updates
   • CSeparating development and operations teams to avoid conflicts
   • DLimiting feedback loops to reduce noise
   Reveal Solution Discussion Select an answer first
2. 7application · medium

   A manufacturing company successfully deployed an AI-based predictive maintenance system in one plant, reducing downtime by 30%. The executive sponsor wants to replicate this success across five other plants. However, plant managers are resistant because they fear the AI will replace their experienced maintenance staff. What should the program manager do to foster adoption and sustain momentum?

   • AFocus only on the plants with the most supportive managers and ignore the others for now.
   • BDeploy the system to all plants simultaneously to achieve consistency and speed.
   • CLaunch a communication campaign that highlights how the AI augments staff expertise and reduces emergency work.
   • DMandate the use of the system across all plants and tie manager bonuses to adoption metrics.
   Reveal Solution Discussion Select an answer first
4. 8foundation · easy

   Which practice is most essential for sustaining an AI initiative over the long term?

   • ADelegating all maintenance to a single technical team
   • BPrioritizing technical performance over stakeholder feedback
   • CEstablishing ongoing governance and periodic alignment with evolving business goals
   • DFreezing the model and business requirements to avoid scope creep
   Reveal Solution Discussion Select an answer first
5. 9application · medium

   A government agency has deployed an AI system for document classification. After initial success, the agency wants to ensure the system remains aligned with changing public policy and continues to meet citizen needs. The agency has a rotating leadership team. What is the most sustainable governance approach?

   • ASchedule an annual review of the AI system's performance and policy alignment.
   • BEstablish a standing AI governance board with representatives from legal, operations, and citizen advocacy groups.
   • CRely on the current leadership team to make all AI decisions during their tenure.
   • DDocument all decisions in a wiki that is updated by the data science team.
   Reveal Solution Discussion Select an answer first
6. 10expert · hard

   A global bank has a successful AI model for detecting money laundering in one country. The model has reduced false positives by 40%. The bank wants to expand this model to other countries. However, each country has different regulatory requirements for data privacy and model explainability. The bank also has limited data science resources. What is the best approach to scale while managing these constraints?

   • AOutsource the model development to a vendor that specializes in each country's regulations.
   • BBuild a separate model from scratch for each country to ensure full compliance.
   • CCreate a federated model architecture that allows local tuning while sharing core learning across countries.
   • DDeploy the same model globally and rely on local compliance teams to handle regulatory issues.
   Reveal Solution Discussion Select an answer first