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Snowflake SnowPro Advanced: Data Scientist Certification Sample Questions:

1. You've developed a binary classification model using Snowpark ML to predict customer subscription renewal (0 for churn, 1 for renew). You want to visualize feature importance using a permutation importance technique calculated within Snowflake. You perform feature permutation and calculate the decrease in model performance (e.g., AUC) after each permutation. Suppose the following query represents the results of this process:

The 'feature_importance_results' table contains the following data:

Based on this output, which of the following statements are the MOST accurate interpretations regarding feature impact and model behavior?

A) Permutation importance only reveals the importance of features within the current model. Different models trained with different features or algorithms might have different feature rankings.
B) The 'contract_length' feature is the most important feature for the model's predictive performance; shuffling it causes the largest drop in AUC.
C) Increasing the 'contract_length' for customers will always lead to a higher probability of renewal. However, there could be correlation between contract length and monthly charges.
D) The 'contract_length' and 'monthly_charges' features are equally important.
E) The 'support_calls' feature is the least important feature; removing it entirely from the model will have little impact on its AUC performance.

2. A retail company is using Snowflake to store transaction data'. They want to create a derived feature called 'customer _ recency' to represent the number of days since a customer's last purchase. The transactions table 'TRANSACTIONS has columns 'customer_id' (INT) and 'transaction_date' (DATE). Which of the following SQL queries is the MOST efficient and scalable way to derive this feature as a materialized view in Snowflake?

A) Option B
B) Option A
C) Option D
D) Option C
E) Option E

3. You're developing a fraud detection system in Snowflake. You're using Snowflake Cortex to generate embeddings from transaction descriptions, aiming to cluster similar fraudulent transactions. Which of the following approaches are MOST effective for optimizing the performance and cost of generating embeddings for a large dataset of millions of transaction descriptions using Snowflake Cortex, especially considering the potential cost implications of generating embeddings at scale? Select two options.

A) Generate embeddings using snowflake-cortex-embed-text function, using the OPENAI embedding model
B) Use a Snowflake Task to incrementally generate embeddings only for new transactions that have been added since the last embedding generation run.
C) Create a materialized view containing pre-computed embeddings for all transaction descriptions.
D) Implement caching mechanism based on a hash of transaction description if transaction description does not change then no need to recompute the emebeddings again.
E) Generate embeddings on the entire dataset every day to capture all potential fraudulent transactions and ensure the model is always up-to-date.

4. You've trained a machine learning model using Scikit-learn and saved it as 'model.joblib'. You need to deploy this model to Snowflake. Which sequence of commands will correctly stage the model and create a Snowflake external function to use it for inference, assuming you already have a Snowflake stage named 'model_stage'?

A) Option B
B) Option A
C) Option D
D) Option C
E) Option E

5. 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 replace=True, packages=['snowflake-snowpark-python', 'pandas', decorator registers the Python function as a Snowpark Stored Procedure, allowing it to be called from SQL.
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.table('CUSTOMER DEMOGRAPHICS')' method creates a local Pandas DataFrame containing a copy of the data from the 'CUSTOMER DEMOGRAPHICS' table.
D) 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.
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.

Solutions: