Learn with Snowflake : DSA-C03 training material for 100% pass

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 'session.table('CUSTOMER DEMOGRAPHICS')' method creates a local Pandas DataFrame containing a copy of the data from the 'CUSTOMER DEMOGRAPHICS' table.
• B. 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.
• 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 'session.sql('SELECT FROM PURCHASE line executes a SQL query against the Snowflake database and returns the results as a list of Row objects.
• E. 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.

You're deploying a pre-built image classification model hosted on a REST API endpoint, and you need to integrate it with Snowflake to classify images stored in cloud storage accessible via an external stage named 'IMAGE STAGE. The API expects image data as a base64 encoded string in the request body. Which SQL query snippet demonstrates the correct approach for calling the external function 'CLASSIFY IMAGE and incorporating the base64 encoding?

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

You are managing a machine learning model lifecycle in Snowflake using the Model Registry. Which of the following statements are true regarding model lineage and governance when utilizing the Model Registry for model versioning and deployment?

• A. The Model Registry automatically tracks the exact SQL queries used to train the model, allowing for full reproducibility of the training process.
• B. Integration with Snowflake's RBAC (Role-Based Access Control) allows for granular control over who can register, update, and deploy model versions.
• C. Custom tags and metadata can be associated with each model version, enabling detailed documentation and traceability of model development and deployment.
• D. Model Registry automatically retrains models based on scheduled data updates, ensuring models are always up-to-date without manual intervention.
• E. The Model Registry provides a central repository to register, version, and manage models, enabling better collaboration and governance across data science teams.

You are tasked with deploying a time series forecasting model within Snowflake using Snowpark Python. The model requires significant pre-processing and feature engineering steps that are computationally intensive. These steps include calculating rolling statistics, handling missing values with imputation, and applying various transformations. You aim to optimize the execution time of these pre- processing steps within the Snowpark environment. Which of the following techniques can significantly improve the performance of your data preparation pipeline?

• A. Write the feature engineering logic directly in SQL and create a view. Use the Snowpark DataFrame API to query the view, avoiding Python code execution within Snowpark.
• B. Convert the Snowpark DataFrame to a Pandas DataFrame using and perform all pre-processing operations using Pandas functions before loading the processed data back to Snowflake.
• C. Ensure that all data used is small enough to fit within the memory of the client machine running the Snowpark Python script, thus removing the need for distributed computing.
• D. Utilize Snowpark's vectorized UDFs and DataFrame operations to leverage Snowflake's distributed computing capabilities.
• E. Force single-threaded execution by setting to avoid overhead associated with parallel processing.

You are working on a customer churn prediction project. One of the features you want to normalize is 'customer_age'. However, a Snowflake table constraint ensures that all 'customer_age' values are between 0 and 120 (inclusive). Furthermore, you want to avoid using any stored procedures and prefer a pure SQL approach for data transformation. Considering these constraints, which normalization technique and associated SQL query is the most appropriate in Snowflake for this scenario, guaranteeing that the scaled values remain within a predictable range?

• A. Box-Cox transformation:
  
• B. Min-Max scaling directly to the range [0, 1] using the known bounds (0 and 120):
  
• C. Min-Max scaling to the range [0, 1]:
  
• D. Z-score standardization after clipping values outside 1 and 99 percentile:
  
• E. Z-score standardization: