Evolution CT-AI Practice Questions (Page 1)

Questions 1–5

1. 1application · medium

   A team is developing a new version of a machine learning model for image recognition. The new model uses a different architecture and was trained on a larger dataset. The team wants to ensure that the new model does not introduce any regressions in the classification of previously supported image categories. Which testing strategy is most appropriate?

   • AAdversarial testing with perturbed images
   • BRegression testing on a representative set of historical images
   • CA/B testing in production with live traffic
   • DLoad testing with a large number of concurrent requests
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2. 2foundation · easy

   In the context of AI-based systems, what does the term 'evolution' primarily refer to?

   • AThe process of replacing the entire system with a new, unrelated model
   • BThe process of making the model's internal logic transparent to users
   • CThe gradual change of the system's behavior over time due to retraining, updates, or changes in the input data distribution
   • DThe initial development phase where the model is first created and trained
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4. 3application · medium

   A team has deployed a model that predicts customer churn. The model was trained on data from the past year. Six months later, the team notices that the model's predictions are less accurate. They suspect that the relationship between customer behavior and churn has changed. What is the most likely cause?

   • AData drift
   • BModel staleness
   • CConcept drift
   • DOverfitting
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5. 4foundation · easy

   What is the primary purpose of continuous monitoring in the context of an evolving AI-based system?

   • ATo ensure the model is never updated again
   • BTo replace the need for all other forms of testing
   • CTo detect changes in the model's performance or data distribution in production
   • DTo automatically retrain the model without human intervention
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6. 5application · medium

   A team is planning to update a machine learning model that is used for real-time fraud detection. The update is expected to improve detection of new fraud patterns but may also increase false positives. The team wants to minimize the risk of degrading the system's overall quality. Which approach should the team take?

   • AImmediately replace the old model with the new one
   • BSkip testing and rely on monitoring after deployment
   • CRun the new model in parallel with the old model and compare outcomes
   • DOnly test the new model on historical data
   Reveal Solution Discussion Select an answer first