Assess AI Readiness Across Key Dimensions CAIPM Practice Questions (Page 10)

Questions 46–50

1. 46application · medium

   A financial services firm wants to use AI for fraud detection. The firm has customer transaction data but it is stored across multiple systems with inconsistent customer identifiers. There is no formal data governance policy. What should the AI program manager do first?

   • AHire more data engineers to manually clean the data.
   • BBuild a data lake to consolidate all data before any AI work.
   • CTrain a model on the data as-is and clean errors later.
   • DEstablish a data governance framework to standardize identifiers and define data quality rules.
   Reveal Solution Discussion Select an answer first
2. 47expert · hard

   A manufacturing company wants to implement real-time quality control using computer vision. The factory floor has high-speed cameras and a local edge server, but the network bandwidth to the cloud is limited. The data is generated in large volumes and is sensitive. The company has a data governance policy, but it does not cover edge-to-cloud data transfer. What is the most critical readiness gap?

   • AInadequate AI model accuracy
   • BLack of a data governance framework for edge-to-cloud data transfer
   • CInsufficient camera resolution for defect detection
   • DLack of cloud computing capacity
   Reveal Solution Discussion Select an answer first
4. 48foundation · easy

   What does 'process and operational readiness' for AI refer to?

   • AThe organization's compliance with data protection laws
   • BThe organization's ability to hire AI specialists
   • CThe number of AI models deployed in production
   • DThe extent to which existing workflows and operations can incorporate AI-driven changes
   Reveal Solution Discussion Select an answer first
5. 49application · medium

   A retail chain completed an AI readiness assessment and found the following: strong executive support, good data quality, but outdated IT infrastructure and a workforce that is resistant to change. The company wants to implement AI for inventory forecasting. What should the program manager prioritize?

   • AAddress the workforce resistance through change management and training.
   • BPrioritize both infrastructure upgrades and workforce change management in parallel.
   • CUpgrade the IT infrastructure first because AI cannot run on outdated systems.
   • DFocus only on the data quality because it is already good.
   Reveal Solution Discussion Select an answer first
6. 50application · medium

   A healthcare startup wants to deploy an AI model for patient risk scoring. The startup uses a cloud platform with GPUs, but the data is stored in an on-premises legacy system with strict data residency requirements. What should the AI program manager recommend?

   • AUse a hybrid architecture: keep data on-premises and use cloud GPUs for training via secure data transfer.
   • BMove all data to the cloud to simplify AI model training.
   • CAbandon the AI project because cloud and on-premises cannot work together.
   • DTrain the model on-premises without GPUs, even if it takes longer.
   Reveal Solution Discussion Select an answer first