Data Readiness for AI Projects Quiz

Answers and explanations appear as you take the quiz.

1. The bank has repayment outcomes only for approved applicants. Which data readiness problem does this show?
   • Duplicate records
   • Selection bias that makes the data unrepresentative
   • A slow API
   • Label disagreement between annotators
2. What is data leakage in the context of model training?
   • Data being stolen by attackers
   • Storage costs rising over time
   • Losing records during migration
   • Features that contain information only available after the outcome being predicted
3. Which GDPR principle limits reusing data collected for one purpose for an incompatible new purpose?
   • Purpose limitation
   • Right to data portability
   • Accountability
   • Storage limitation
4. In Data Readiness for AI Projects: what does Cohen's kappa measure?
   • The speed of a data pipeline
   • The number of missing values
   • Agreement between annotators, corrected for chance
   • The cost of labeling per item
5. Why does retrieval for a generative AI assistant need to respect existing access permissions?
   • To make responses load faster
   • So the assistant does not reveal documents a user is not allowed to see
   • Because embeddings need permissions to be calculated
   • To lower the number of documents stored
6. Why should forecasting models usually be tested on data split by time rather than randomly?
   • It mirrors real deployment and avoids training on future information
   • Random splits are illegal
   • Time splits need less data
   • Random splits cannot be used with labels
7. Which statement reflects a common misconception about data readiness?
   • Readiness depends on the specific use case
   • Quality only needs to be good enough for the task
   • Legal rights to use data must be checked
   • A large volume of data means an organization is ready for AI
8. What does a 'datasheets for datasets' document record?
   • The cost of each database server
   • The model's accuracy on the test set
   • A dataset's origin, composition, collection method and intended use
   • The passwords for accessing the data