You have a binary classification model deployed in Snowflake to predict customer churn. The model outputs a probability score between 0 and 1. You've calculated the following confusion matrix on a holdout set: I I Predicted Positive I Predicted Negative I --1 1 Actual Positive | 80 | 20 | I Actual Negative | 10 | 90 | What are the Precision, Recall, and Accuracy for this model, and what do these metrics tell you about the model's performance? SELECT statement given for true and false condition (True Positive, True Negative, False Positive, False Negative)

• A. Precision = 0.89, Recall = 0.80, Accuracy = 0.85. The model has good overall performance with balanced precision and recall.
• B. Precision = 0.89, Recall = 0.80, Accuracy = 0.85. The model is slightly better at avoiding false positives than identifying true positives.
  
• C. Precision = 0.80, Recall = 0.90, Accuracy = 0.90. The model is performing poorly, with a high rate of both false positives and false negatives.
• D. Precision = 0.80, Recall = 0.89, Accuracy = 0.85. The model is slightly better at identifying true positives than avoiding false positives.
• E. Precision = 0.90, Recall = 0.80, Accuracy = 0.80. The model has good overall performance but needs to be adjusted to improve the false negative rate.

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You are deploying a pre-trained image classification model stored as a serialized file in an internal stage within Snowflake. You need to create a UDF to load this model and use it for inference on image data stored in a VARIANT column. The model was trained using Python's scikit-learn library and uses OpenCV for image processing. Which of the following code snippets correctly outlines the steps required to create and deploy this UDF? Assume you have already created an internal stage named 'MODEL STAGE and uploaded the model file into it. You also need to create a temporary directory that will be removed after the execution.

• A.
  
• B.
  
• C.
  
• D.
  
• E.
  

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You are tasked with deploying a fraud detection model in Snowflake using the Model Registry. The model is trained on a dataset that is updated daily. You need to ensure that your deployed model uses the latest approved version and that you can easily roll back to a previous version if any issues arise. Which of the following approaches would provide the most robust and maintainable solution for model versioning and deployment, considering minimal downtime during updates and rollback?

• A. Use Snowflake Tasks to periodically refresh a table containing the latest model weights. The UDF directly queries this table for predictions.
• B. Create multiple Snowflake UDFs, each corresponding to a different model version. Manually switch the active UDF by updating application code when a new model is deployed.
• C. Store all model versions within a single model registry entry without versioning, overwriting the existing file with each new training run.
• D. Register each new model version in the Snowflake Model Registry and promote the desired version to 'PRODUCTION' stage. Update a single UDF that dynamically fetches the model based on the 'PRODUCTION' stage metadata.
• E. Deploy a new Snowflake UDF referencing the model file directly in cloud storage every time the model is retrained. Rely on cloud storage versioning for rollback.

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You are tasked with deploying a fraud detection model in Snowflake using the Model Registry. The model is trained on a dataset that is updated daily. You need to ensure that your deployed model uses the latest approved version and that you can easily roll back to a previous version if any issues arise. Which of the following approaches would provide the most robust and maintainable solution for model versioning and deployment, considering minimal downtime during updates and rollback?

• A. Use Snowflake Tasks to periodically refresh a table containing the latest model weights. The UDF directly queries this table for predictions.
• B. Create multiple Snowflake UDFs, each corresponding to a different model version. Manually switch the active UDF by updating application code when a new model is deployed.
• C. Store all model versions within a single model registry entry without versioning, overwriting the existing file with each new training run.
• D. Register each new model version in the Snowflake Model Registry and promote the desired version to 'PRODUCTION' stage. Update a single UDF that dynamically fetches the model based on the 'PRODUCTION' stage metadata.
• E. Deploy a new Snowflake UDF referencing the model file directly in cloud storage every time the model is retrained. Rely on cloud storage versioning for rollback.

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You've trained a binary classification model in Snowflake to predict loan defaults. You need to understand which features are most influential in the model's predictions for individual loans. Which of the following methods provide insight into model explainability, AND how can they be leveraged within the Snowflake environment? (Select all that apply)

• A. LIME (Local Interpretable Model-agnostic Explanations): Can be implemented by creating a UDF (User-Defined Function) in Snowflake that takes a loan's feature values as input and returns the feature importance scores for that specific loan, based on the LIME algorithm applied to the model's predictions.
• B. SHAP (SHapley Additive explanations): Similar to LIME, SHAP values can be calculated using a Snowflake UDF, providing a more comprehensive and theoretically grounded explanation of each feature's contribution to the prediction, considering all possible feature combinations.
• C. Decision Tree visualization: Convert the model to decision trees and visualize it.
• D. Permutation Feature Importance: Directly supported within Snowflake ML's model evaluation functions, allowing you to rank features based on their impact on model performance when their values are randomly shuffled.
• E. Coefficient analysis: By inspecting the coefficients of a linear model, we can easily determine feature importances.

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