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You are analyzing a dataset of website traffic and conversions in Snowflake, aiming to understand the relationship between the number of pages visited CPAGES VISITED) and the conversion rate (CONVERSION_RATE). You perform a simple linear regression using the 'REGR SLOPE and 'REGR INTERCEPT functions. However, after plotting the data and the regression line, you observe significant heteroscedasticity (non-constant variance of errors). Which of the following actions, performed within Snowflake during the data preparation and feature engineering phase, are MOST appropriate to address this heteroscedasticity and improve the validity of your linear regression model? (Select all that apply)

• A. Standardize the 'PAGES_VISITED' and 'CONVERSION_RATE variables using the and functions.Create OR REPLACE VIEW STANDARDIZED_DATA AS SELECT (PAGES_VISITED - OVER()) / OVER() AS Z PAGES_VISITED, (CONVERSION RATE -OVER()) / OVER() AS FROM ORIGINAL_DATA;
• B. Apply a logarithmic transformation to the 'CONVERSION RATE' variable using the 'LN()' function. CREATE OR REPLACE VIEW TRANSFORMED_DATA AS SELECT PAGES VISITED, LN(CONVERSION RATE) AS LOG_CONVERSION RATE FROM ORIGINAL_DATA;
• C. Calculate the weighted least squares regression by weighting each observation by the inverse of the squared predicted values from an initial OLS regression. This requires multiple SQL queries.
• D. Remove outlier data points from the dataset based on the Interquartile Range (IQR) of the residuals from the original linear regression model. This requires calculating the residuals first.
• E. Apply a Box-Cox transformation to the 'CONVERSION RATE' variable. This transformation will determine the optimal lambda value using some complex SQL statistical operations. This can be approximated to log tranformation in many real life scenarios.

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You have deployed a machine learning model in Snowflake to predict customer churn. The model was trained on data from the past year. After six months of deployment, you notice the model's recall for identifying churned customers has dropped significantly. You suspect model decay. Which of the following Snowflake tasks and monitoring strategies would be MOST appropriate to diagnose and address this model decay?

• A. Implement a Shadow Deployment strategy in Snowflake. Route a small percentage of incoming data to both the existing model and a newly trained model. Compare the predictions from both models using a UDF that calculates the difference in predicted probabilities. Trigger an alert if the differences exceed a certain threshold.
• B. Back up the original training data to secure storage. Ingest all new data as it comes in. Retrain a new model and compare its performance with the backed-up training data.
• C. Establish a Snowflake pipe to continuously ingest feedback data (actual churn status) into a feedback table. Write a stored procedure to calculate performance metrics (e.g., recall, precision) on a sliding window of recent data. Create a Snowflake Alert that triggers when recall falls below a defined threshold.
• D. Use Snowflake's data sharing feature to share the model's predictions with a separate analytics team. Let them monitor the overall customer churn rate and notify you if it changes significantly.
• E. Create a Snowflake Task that automatically retrains the model weekly with the most recent six months of data. Monitor the model's performance metrics using Snowflake's query history to track the accuracy of the predictions.

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You are tasked with performing exploratory data analysis on a table named containing daily sales transactions. The table includes columns like 'transaction_date', 'product_id', 'quantity' , and 'price'. Your goal is to identify potential data quality issues and understand the distribution of sales. Which of the following SQL queries using Snowflake's statistical functions and features would be MOST effective for quickly identifying outliers in the 'quantity' column, potential data skewness, and missing values?

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

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You have trained a logistic regression model in Python using scikit-learn and plan to deploy it as a Python stored procedure in Snowflake. You need to serialize the model for deployment. Consider the following code snippet:

• A. The code will fail because Snowflake stages cannot be used to store model objects.
• B. The code will fail because it does not handle potential security vulnerabilities associated with deserializing pickled objects from untrusted sources.
• C. The code will fail because the 'model_bytes' variable is not accessible within the 'predict' function's scope.
• D. The code will execute successfully. The model serialization and deserialization using pickle are correctly implemented within the stored procedure.
• E.
  

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You are deploying a fraud detection model hosted on a third-party ML platform and accessing it via an external function in Snowflake. The model API has a strict rate limit of 10 requests per second. To prevent exceeding this limit and ensure smooth operation, what strategies could you implement within Snowflake, considering performance and cost implications? Select all that apply.

• A. Implement a retry mechanism within the external function definition to handle API rate limit errors (e.g., HTTP 429 errors) using exponential backoff.
• B. Utilize Snowflake's built-in caching mechanism for the external function results. This reduces the number of calls to the external API for repeated input data.
• C. Implement a custom queueing system within Snowflake using temporary tables and stored procedures to batch requests and send them to the external function at a controlled rate.
• D. Scale up the Snowflake virtual warehouse to the largest size possible. This will allow for more concurrent requests without exceeding the rate limit.
• E. Implement a UDF (User-Defined Function) that sleeps for 0.1 seconds before each call to the external function. This guarantees a maximum rate of 10 requests per second.

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