AI Models, Considerations, and Requirements AAIA Practice Questions (Page 2)

Questions 6–10

1. 6expert · hard

   A healthcare startup is developing an AI model to diagnose skin conditions from images. The training data is primarily from light-skinned patients, leading to potential bias. The startup wants to ensure the model is fair and reliable for all skin types. Which action is most appropriate?

   • ACollect additional diverse training data and implement fairness constraints during training.
   • BIncrease the model's complexity to better handle diverse skin types.
   • CDeploy the model and monitor for complaints from users with darker skin tones.
   • DUse the current data and rely on post-hoc bias mitigation techniques.
   Reveal Solution Discussion Select an answer first
2. 7expert · hard

   A credit card company is evaluating two fraud detection models. Model A has higher precision but lower recall. Model B has higher recall but lower precision. The company's cost of investigating false positives is high, but the cost of missing fraud is also significant. Which model should the company choose?

   • AChoose Model A because precision is more important than recall.
   • BChoose the model with the higher F1 score.
   • CChoose Model B because catching more fraud is always better.
   • DChoose the model that minimizes the total cost of false positives and false negatives.
   Reveal Solution Discussion Select an answer first
4. 8foundation · easy

   Which of the following is a common risk associated with AI models that can lead to systematic errors for certain groups?

   • ADatabase deadlocks
   • BHardware failure
   • CNetwork latency
   • DBias and fairness issues
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5. 9application · medium

   A bank is required by regulators to explain why a loan application was denied by its AI model. The model is a complex ensemble. The bank needs to provide a clear explanation to the customer. Which technique is most appropriate?

   • AProvide the model's overall accuracy as the explanation.
   • BState that the decision was made by an AI model without further detail.
   • CUse SHAP values to identify the contribution of each feature to the decision.
   • DShow the model's training data to the customer.
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6. 10expert · hard

   A large retailer has deployed a demand forecasting model. Six months after deployment, the model's performance has degraded significantly. The data science team suspects concept drift due to changing consumer behavior. They also notice that the model's retraining pipeline is not automated. The retailer wants to minimize downtime and ensure the model remains reliable. Which combination of actions is most effective?

   • AImplement automated monitoring and a retraining pipeline that triggers when drift is detected.
   • BIncrease the model's complexity to better fit recent data.
   • CManually retrain the model every quarter regardless of performance.
   • DReplace the model with a simpler linear model to reduce complexity.
   Reveal Solution Discussion Select an answer first