Pass with professional DSA-C03 actual quiz materials

You are building a customer churn prediction model for a telecommunications company. You have a 'CUSTOMER DATA' table with a 'MONTHLY SPENDING' column that represents the customer's monthly bill amount. You want to binarize this column to create a feature indicating whether a customer is a 'High Spender' or 'Low Spender'. You decide that customers spending more than $75 are 'High Spenders'. Which of the following Snowflake SQL statements is the most efficient and correct way to achieve this, considering performance and readability, while avoiding potential NULL values in the resulting binarized column?

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

You are using Snowflake Cortex to perform sentiment analysis on customer reviews stored in a table called 'CUSTOMER REVIEWS' The table has a column containing the text of each review. You want to create a user-defined function (UDF) to extract sentiment score between the range of -1 to 1 using the 'snowflake_cortex.sentiment' function in Snowflake Cortex. Which of the following UDF definitions would correctly implement this, allowing it to be called directly on the column?

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

You have a table in Snowflake named 'CUSTOMER DATA' with columns 'CUSTOMER D', 'PURCHASE AMOUNT', and 'RECENCY'. You want to perform feature scaling on 'PURCHASE AMOUNT' using Min-Max scaling and store the scaled values in a new column named 'SCALED PURCHASE _ AMOUNT'. Which of the following Snowflake SQL code snippets correctly implements this feature scaling? Note: Assume there are no NULL values in PURCHASE AMOUNT and you have privileges to create temporary tables and UDFs if necessary.

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

You've trained a sentiment analysis model in Snowflake using Snowpark Python and deployed it as a UDF. After several weeks, you notice the model's performance has degraded significantly. You suspect concept drift. Which of the following actions represent the MOST effective and comprehensive approach to address this situation, considering the entire Machine Learning Lifecycle, including monitoring, retraining, and model versioning? Assume you have monitoring in place that alerted you to the drift.

• A. Analyze the recent data to understand the nature of the concept drift, retrain the model with a combination of historical and recent data, version the new model, and perform AIB testing against the existing model before fully deploying the new version. Log both model version predictions during AIB testing.
• B. Disable the model and revert to a rule-based system, abandoning the machine learning approach altogether.
• C. Retrain the model on a sample of the most recent data, overwriting the original model files in your Snowflake stage and updating the UDF definition. Keep no record of the old model.
• D. Adjust the existing model's parameters manually to compensate for the observed performance degradation without retraining or versioning.
• E. Immediately replace the current UDF with a newly trained model using the latest data, ignoring model versioning and assuming the latest data will solve the drift issue.

You are tasked with training a machine learning model within Snowflake using a Python UDTF. The UDTF is intended to process incoming sales data, calculate features, and update the model incrementally. The model is a simple linear regression using scikit-learn. Your initial attempt fails with a 'ModuleNotFoundError: No module named 'sklearn" error within the UDTF. You have already confirmed that scikit-learn is available in your Anaconda channel and specified it during session creation. Which of the following actions would MOST directly address this issue and allow the UDTF to successfully import and use scikit-learn?

• A. Recreate the Anaconda environment and ensure that the 'sklearn' package is installed specifically within the environment's 'site-packages' directory. Then, recreate the Snowflake session.
• B. Include ' import snowflake.snowpark; session = snowflake.snowpark.session.get_active_session()' within the UDTF code to explicitly initialize the Snowpark session before importing sklearn. Ensure that scikit-learn is included in the 'imports' argument of the 'create_dataframe' method.
• C. Explicitly copy the 'sklearn' directory and its dependencies directly into the same directory as your UDTF definition script on the Snowflake stage, then reference them using relative paths within the UDTF.
• D. Ensure that the Anaconda channel containing 'sklearn' is explicitly activated at the account level using the 'ALTER ACCOUNT command. Verify the channel is listed in 'SHOW CHANNELS'.
• E. When creating the UDTF, use the 'PACKAGES' parameter to explicitly specify the 'skiearn' package. For example: 'CREATE OR REPLACE FUNCTION RETURNS TABLE LANGUAGE PYTHON RUNTIME_VERSION = '3.8' PACKAGES = ('snowflake-snowpark-python','scikit-learn') ...