You are working with a Snowflake table named 'CUSTOMER DATA' containing customer information, including a 'PHONE NUMBER' column. Due to data entry errors, some phone numbers are stored as NULL, while others are present but in various inconsistent formats (e.g., with or without hyphens, parentheses, or country codes). You want to standardize the 'PHONE NUMBER column and replace missing values using Snowpark for Python. You have already created a Snowpark DataFrame called 'customer df representing the 'CUSTOMER DATA' table. Which of the following approaches, used in combination, would be MOST efficient and reliable for both cleaning the existing data and handling future data ingestion, given the need for scalability?

• A. Create a Snowflake Stored Procedure in SQL that uses regular expressions and 'CASE statements to format the "PHONE_NUMBER column and replace NULL values. Call this stored procedure from a Snowpark Python script.
• B. Leverage Snowflake's data masking policies to mask any invalid phone number and create a view that replaces NULL values with 'UNKNOWN'. This approach doesn't correct existing data but hides the issue.
• C. Use a UDF (User-Defined Function) written in Python that formats the phone numbers based on a regular expression and applies it to the DataFrame using For NULL values, replace them with a default value of 'UNKNOWN'.
• D. Use a series of and methods on the Snowpark DataFrame to handle NULL values and different phone number formats directly within the DataFrame operations.
• E. Create a Snowflake Pipe with a COPY INTO statement and a transformation that uses a SQL function within the COPY INTO statement to format the phone numbers and replace NULL values during data loading. Also, implement a Python UDF for correcting already existing data.

Explanation: Only visible for BraindumpsPass members. You can sign-up / login (it's free).

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. 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.
• B. 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;
• C. 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.
• D. 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;
• E. 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.

Explanation: Only visible for BraindumpsPass members. You can sign-up / login (it's free).

You are building a machine learning model to predict customer churn for a telecommunications company. One of the features is 'tariff_plan', which is a string representing different tariff plans (e.g., 'Basic', 'Premium', 'Unlimited'). You need to encode this feature for your model, but you also want to handle potential new tariff plans that might appear in future data'. Which encoding method and Snowflake SQL approach would be MOST suitable to minimize dimensionality and address unseen values effectively, assuming the number of plans is moderately high (around 20-30)?

• A. Hash Encoding (Feature Hashing) using a UDF in Snowflake, with a fixed number of features and a hashing function to map each tariff plan to a feature index, accepting potential collisions. Handle new tariff plans naturally through the hashing function.
• B. One-Hot Encoding using CREATE OR REPLACE VIEW, handling new values by NULLIF('Unknown', tariff_plan) before encoding, potentially leading to a high number of columns.
• C. Binary Encoding using a UDF to convert each tariff plan into binary code, storing encoded results into snowflake, then splitting the binary representation into separate columns.
• D. Label Encoding using a UDF (User-Defined Function) with a predefined mapping, assigning a new integer to unseen values, and storing the mapping in a separate table in Snowflake.
• E. Target Encoding (Mean Encoding) using Snowflake SQL, calculating the mean churn rate for each tariff plan and using that as the encoded value. Handle unseen values with the global mean churn rate, being mindful of potential target leakage.

Explanation: Only visible for BraindumpsPass members. You can sign-up / login (it's free).

Consider the following Python UDF intended to train a simple linear regression model using scikit-learn within Snowflake. The UDF takes feature columns and a target column as input and returns the model's coefficients and intercept as a JSON string. You are encountering an error during the CREATE OR REPLACE FUNCTION statement because of the incorrect deployment of the package during runtime. What would be the right way to fix this deployment and execute your model?

• A. The package 'scikit-learn' needs to be included in the import statement and deployed while creation of the 'Create or Replace function' statement, by including parameter. Also the correct code is to ensure the model can be trained and return the coefficients and intercept of the model.
• B. The code works seamlessly without modification as Snowflake automatically resolves all the dependencies and ensures the execution of code within the create or replace function statement.
• C. The required packages 'scikit-learn' is not present. The correct way to create UDF is by including the import statement within the function along with the deployment.
• D. The package 'scikit-learn' needs to be included in the import statement and deployed while creation of the 'Create or Replace function' statement, by including parameter. Also the correct code is to ensure the model can be trained and return the coefficients and intercept of the model.
• E. The package 'scikit-learn' needs to be included in the import statement and deployed while creation of the 'Create or Replace function' statement, by including parameter. Also the correct code is to ensure the model can be trained and return the coefficients and intercept of the model.

Explanation: Only visible for BraindumpsPass members. You can sign-up / login (it's free).

You are tasked with building a machine learning pipeline in Snowpark Python to predict customer lifetime value (CLTV). You need to access and manipulate data residing in multiple Snowflake tables and views, including customer demographics, purchase history, and website activity. To improve code readability and maintainability, you decide to encapsulate data access and transformation logic within a Snowpark Stored Procedure. Given the following Python code snippet representing a simplified version of your stored procedure:

• A. The 'snowflake.snowpark.context.get_active_session()' function retrieves the active Snowpark session object, enabling interaction with the Snowflake database from within the stored procedure.
• B. The 'session.table('CUSTOMER DEMOGRAPHICS')' method creates a local Pandas DataFrame containing a copy of the data from the 'CUSTOMER DEMOGRAPHICS' table.
• C. The 'session.write_pandas(df, table_name='CLTV PREDICTIONS', auto_create_table=Truey function writes the Pandas DataFrame 'df containing the CLTV predictions directly to a new Snowflake table named , automatically creating the table if it does not exist.
• D. The replace=True, packages=['snowflake-snowpark-python', 'pandas', decorator registers the Python function as a Snowpark Stored Procedure, allowing it to be called from SQL.
• E. The 'session.sql('SELECT FROM PURCHASE line executes a SQL query against the Snowflake database and returns the results as a list of Row objects.

Explanation: Only visible for BraindumpsPass members. You can sign-up / login (it's free).