Flexibility and Adaptability CT-AI Practice Questions (Page 3)

Questions 11–15

1. 11foundation · easy

   Which of the following is an example of an AI system demonstrating flexibility rather than adaptability?

   • AA recommendation system that updates its user profiles based on recent clicks.
   • BA speech recognition system that accurately transcribes both British and American English accents without any model update.
   • CA fraud detection model that is fine-tuned with new fraud patterns every month.
   • DA spam filter that is retrained weekly on newly reported spam emails.
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2. 12foundation · easy

   An AI-based fraud detection system is initially trained on historical transaction data. To maintain accuracy, it must be periodically retrained with new fraud patterns. This is an example of:

   • AFlexibility, because the system handles new transaction types without retraining.
   • BAdaptability, because the system updates its model to learn from new data.
   • CRobustness, because the system can handle noisy input data.
   • DExplainability, because the system provides explanations for its fraud predictions.
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4. 13application · medium

   A team is testing a language translation app that is used by travelers. The app must handle various dialects and slang. The team wants to ensure the app does not degrade when faced with informal language. Which testing approach is most appropriate?

   • AInclude test cases with informal language, slang, and various dialects in the test suite.
   • BTest the app only with formal language to ensure baseline quality.
   • CMeasure the app's response time under heavy load.
   • DRetrain the app with informal language data before testing.
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5. 14expert · hard

   A medical diagnosis AI system is designed to adapt to new research findings by updating its model. However, the system must also provide explanations for its decisions to clinicians. The team notices that after each update, the explanations become less consistent with previous versions, making it harder for clinicians to trust the system. Which trade-off is the team facing?

   • AFlexibility vs. explainability, because the system must adjust to new contexts.
   • BAdaptability vs. explainability, because updates can change the reasoning behind decisions.
   • CFlexibility vs. robustness, because the system must handle new inputs without failing.
   • DReliability vs. adaptability, because updates may introduce errors.
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6. 15foundation · easy

   Why are flexibility and adaptability considered critical quality characteristics for AI-based systems in dynamic environments?

   • AThey ensure the system's performance remains stable even when the environment or data distribution changes over time.
   • BThey are only important for systems deployed in cloud environments, not on-premises.
   • CThey eliminate the need for continuous monitoring and testing of the AI system.
   • DThey guarantee that the system will never make a wrong prediction.
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