AI Solution Development Methodologies and Lifecycle AAIA Practice Questions (Page 6)

Questions 26–30

1. 26expert · hard

   A financial trading firm has developed a model to predict stock price movements. The model must make predictions in real-time with minimal latency. The firm also needs to comply with regulations that require all trading decisions to be logged and auditable. The model is currently deployed as a batch process that runs every hour. The firm wants to move to real-time predictions. What is the most important consideration for the deployment strategy?

   • ADeploy the model on the same servers as the trading application to reduce network latency
   • BUse a larger batch window to reduce the frequency of model updates
   • CUse a simpler model to reduce inference time, even if accuracy decreases
   • DEnsure the model can be deployed as a low-latency API and that all predictions are logged for audit
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2. 27foundation · easy

   During model development, which dataset is typically used to tune hyperparameters and make decisions about model selection?

   • ATest dataset
   • BTraining dataset
   • CValidation dataset
   • DUnlabeled dataset
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4. 28foundation · easy

   Why is data quality and governance important in the data preparation phase of an AI project?

   • AIt ensures the data is stored in a cloud environment.
   • BIt reduces the need for model monitoring after deployment.
   • CIt helps ensure the data is accurate, consistent, and compliant with regulations, which directly impacts model performance and trust.
   • DIt guarantees the model will be 100% accurate.
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5. 29foundation · easy

   Which AI lifecycle phase is primarily concerned with collecting, cleaning, and organizing the data needed to train and evaluate the model?

   • AProblem definition
   • BDeployment
   • CModel development
   • DData acquisition
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6. 30application · medium

   A healthcare organization has deployed a patient readmission prediction model. Six months later, the model's accuracy has declined because patient demographics and treatment protocols have changed. The organization needs to maintain the model's effectiveness while ensuring that any changes are traceable for regulatory audits. Which combination of actions should the organization take?

   • AIncrease the frequency of manual reviews of model outputs without changing the model
   • BRetrain the model with the latest data and deploy it immediately to restore accuracy
   • CRoll back to the original model and freeze all updates to ensure stability
   • DMonitor for drift, retrain with recent data, and version the model with full documentation of changes
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