You are developing a fraud detection model in Snowflake. You've identified that transaction amounts and transaction frequency are key features. You observe that the transaction amounts are heavily right-skewed and the transaction frequencies have outliers. Furthermore, the model needs to be robust against seasonal variations in transaction frequency. Which of the following feature engineering steps, when applied in sequence, would be MOST appropriate to handle these data characteristics effectively?

• A. 1. Apply a Box-Cox transformation to the transaction amounts. 2. Apply a quantile-based transformation (e.g., using NTILE) to the transaction frequencies to map them to a uniform distribution. 3. Calculate the difference between the current transaction frequency and the average transaction frequency for that day of the week over the past year.
• B. 1. Apply min-max scaling to the transaction amounts. 2. Remove outliers in transaction frequency using the Interquartile Range (IQR) method. 3. Calculate the cumulative sum of transaction frequencies.
• C. 1. Apply a square root transformation to the transaction amounts. 2. Standardize the transaction frequencies using Z-score normalization. 3. Create dummy variables for the day of the week.
• D. 1. Apply a logarithmic transformation to the transaction amounts. 2. Apply a Winsorization technique to the transaction frequencies to handle outliers. 3. Calculate a rolling average of transaction frequency over a 7-day window.
• E. 1. Apply a logarithmic transformation to the transaction amounts. 2. Replace outliers in transaction frequency with the mean value. 3. Create lag features of transaction frequency for the previous 7 days.

You are using Snowflake Cortex to analyze customer reviews. You have created a vector embedding for each review using a UDF that calls a remote LLM inference endpoint. Now you need to perform a similarity search to identify reviews that are similar to a given query review. Which of the following SQL queries leveraging vector functions in Snowflake is the MOST efficient and appropriate way to achieve this, assuming the 'REVIEW EMBEDDINGS' table has columns 'review_id' and 'embedding' (a VECTOR column) and query_embedding' is a pre-computed vector embedding?

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

You are building a model to predict loan defaults using a dataset stored in Snowflake. After training your model and calculating residuals, you create a scatter plot of the residuals against the predicted values. The plot shows a cone-shaped pattern, with residuals spreading out more as the predicted values increase. Which of the following SQL queries, run within a Snowpark Python session, could be used to address the underlying issue indicated by this residual pattern, assuming the predicted values are stored in a column named and the residuals in a column named 'loan_default_residuar in a Snowflake table named 'loan_predictionds'?

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

You are validating a time series forecasting model for daily sales using Snowflake and Snowpark. The residuals plot shows a clear sinusoidal pattern. Which of the following actions should you consider to improve your model? (Select all that apply)

• A. Apply a Box-Cox transformation to the target variable (sales) to stabilize the variance.
• B. Incorporate lagged features representing previous sales values (e.g., sales from the previous day, week, or month).
• C. Increase the regularization strength in your model.
• D. Remove outlier data points to improve overall model performance.
• E. Change the algorithm to a linear regression model, since it is more likely to capture sinusoidal patterns