Snowflake : DSA-C03 Questions & Answers

A data scientist is analyzing website traffic data stored in Snowflake. The data includes daily page views for different pages. The data scientist suspects that the variance of page views for a particular page, 'home', has significantly increased recently. Which of the following steps and Snowflake SQL queries could be used to identify a potential change in the variance of 'home' page views over time (e.g., comparing variance before and after a specific date)? Select all that apply.

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

You are training a Gradient Boosting model within Snowflake using Snowpark Python to predict customer churn. You are using the Hyperopt library for hyperparameter tuning. You want to use the function to find the best hyperparameters. You have defined your objective function, , and the search space, Which of the following is the MOST efficient and correct way to call the function within a Snowpark Python UDF to ensure the Hyperopt trials data is effectively managed and accessible for further analysis within Snowflake?

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

You are using Snowflake ML to predict housing prices. You've created a Gradient Boosting Regressor model and want to understand how the 'location' feature (which is categorical, representing different neighborhoods) influences predictions. You generate a Partial Dependence Plot (PDP) for 'location'. The PDP shows significantly different predicted prices for each neighborhood. Which of the following actions would be MOST appropriate to further investigate and improve the model's interpretability and performance?

• A. Combine the PDP for 'location' with a two-way PDP showing the interaction between 'location' and 'square_footage'.
• B. Use one-hot encoding for the 'location' feature and generate individual PDPs for each one-hot encoded column.
• C. Generate ICE (Individual Conditional Expectation) plots alongside the PDP to assess the heterogeneity of the relationship between 'location' and predicted price.
• D. Remove the 'location' feature from the model, as categorical features are inherently difficult to interpret.
• E. Replace the 'location' feature with a numerical feature representing the average house price in each neighborhood, calculated from historical data.

You've developed a fraud detection model using Snowflake ML and want to estimate the expected payout (loss or gain) based on the model's predictions. The cost of investigating a potentially fraudulent transaction is $50. If a fraudulent transaction goes undetected, the average loss is $1000. The model's confusion matrix on a validation dataset is: Predicted Fraud Predicted Not Fraud Actual Fraud 150 50 Actual Not Fraud 20 780 Which of the following SQL queries in Snowflake, assuming you have a table 'FRAUD PREDICTIONS' with columns 'TRANSACTION ID', 'ACTUAL FRAUD', and 'PREDICTED FRAUD' (1 for Fraud, O for Not Fraud), provides the most accurate estimate of the expected payout for every 1000 transactions?

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

You are evaluating a binary classification model's performance using the Area Under the ROC Curve (AUC). You have the following predictions and actual values. What steps can you take to reliably calculate this in Snowflake, and which snippet represents a crucial part of that calculation? (Assume tables 'predictions' with columns 'predicted_probability' (FLOAT) and 'actual_value' (BOOLEAN); TRUE indicates positive class, FALSE indicates negative class). Which of the below code snippet should be used to calculate the 'True positive Rate' and 'False positive Rate' for different thresholds

• A. Export the 'predicted_probability' and 'actual_value' columns to a local Python environment and calculate the AUC using scikit-learn.
• B. Calculate AUC directly within a Snowpark Python UDF using scikit-learn's function. This avoids data transfer overhead, making it highly efficient for large datasets. No further SQL is needed beyond querying the predictions data.
  
• C. The best way to calculate AUC is to randomly guess the probabilities and see how it performs.
• D. The AUC cannot be reliably calculated within Snowflake due to limitations in SQL functionality for statistical analysis.
• E. Using only SQL, Create a temporary table with calculated True Positive Rate (TPR) and False Positive Rate (FPR) at different probability thresholds. Then, approximate the AUC using the trapezoidal rule.