Assess AI Readiness Across Key Dimensions CAIPM Practice Questions (Page 6)

Questions 26–30

1. 26foundation · easy

   Which of the following is NOT a typical primary dimension in AI readiness assessment?

   • AStrategy alignment
   • BProcess and operational readiness
   • CWorkforce skills and culture
   • DMarketing budget allocation
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2. 27foundation · easy

   An organization's leadership wants to assess whether its AI initiatives are aligned with its strategic goals. Which question is most relevant to this assessment?

   • AAre the AI team members certified in machine learning?
   • BDo the AI initiatives directly support the organization's key business objectives?
   • CDoes the organization have enough GPUs for training?
   • DAre the AI models trained on the latest data?
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4. 28application · medium

   A financial services firm wants to introduce AI-powered fraud detection. The compliance team is worried about explainability, while the data science team wants to deploy a complex deep learning model. The firm has a strong data infrastructure and clear processes, but there is no established culture of cross-functional collaboration. What is the most significant people/culture readiness gap?

   • ALack of a formal AI strategy
   • BInadequate computing resources
   • CLack of trust and collaboration between compliance and data science teams
   • DInsufficient data science skills
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5. 29application · medium

   A manufacturing company wants to implement predictive maintenance on its production line. The plant engineers are skeptical of AI, fearing it will replace their jobs. The IT team has the necessary sensors and data pipelines, but the maintenance workflow is still paper-based. As the AI program manager, which two dimensions should you prioritize in your readiness assessment?

   • ATechnology and processes
   • BStrategy and data
   • CPeople and processes
   • DData and technology
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6. 30application · medium

   A retail company wants to deploy a real-time recommendation engine. The current on-premises servers are at capacity, and the data team uses a mix of legacy databases. The company has a limited budget and cannot afford a major infrastructure overhaul. What should the AI program manager recommend?

   • ABuild the recommendation engine on the existing on-premises servers.
   • BOutsource the entire AI project to a third-party vendor.
   • CUse a hybrid approach: move only the AI workloads to a scalable cloud platform while keeping legacy systems on-premises.
   • DMigrate all workloads to a hyperscale public cloud immediately.
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