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Snowflake DSA-C03 Exam Syllabus Topics:

Snowflake SnowPro Advanced: Data Scientist Certification Sample Questions:

1. You are building a customer churn prediction model in Snowflake using Snowflake ML. After training, you need to evaluate the model's performance and identify areas for improvement. Given the following table 'PREDICTIONS' contains predicted probabilities and actual churn labels, which SQL query effectively calculates both precision and recall for the churn class (where 'CHURN = 1')?

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

2. You are building a churn prediction model for a telecommunications company using Snowflake and Snowpark ML. You have trained a Gradient Boosting Machine (GBM) model and want to understand the feature importance to identify key drivers of churn. You've used SHAP (SHapley Additive exPlanations) values to explain individual predictions. Given a customer with a high churn risk, you observe that the 'monthly_charges' feature has a significantly large negative SHAP value for that specific prediction. Which of the following statements best interprets this observation in the context of feature impact?

A) The negative SHAP value suggests 'monthly_charges' interacts with other features. Its precise impact is conditional and cannot be generalized without further analysis of feature interaction effects with SHAP values.
B) The 'monthly_charges' feature has no impact on the customer's churn probability.
C) The negative SHAP value indicates that 'monthly_charges' is negatively correlated with all customers' churn probability, irrespective of their individual profile.
D) Increasing 'monthly_charges' for this customer is likely to decrease their probability of churning.
E) Increasing 'monthly_charges' for this customer is likely to increase their probability of churning.

3. 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.sql('SELECT FROM PURCHASE line executes a SQL query against the Snowflake database and returns the results as a list of Row objects.
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 replace=True, packages=['snowflake-snowpark-python', 'pandas', decorator registers the Python function as a Snowpark Stored Procedure, allowing it to be called from SQL.
D) 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.
E) 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.

4. You have a table 'PRODUCT SALES in Snowflake with columns: 'PRODUCT (INT), 'SALE_DATE (DATE), 'SALES_AMOUNT (FLOAT), and 'PROMOTION FLAG' (BOOLEAN). You need to perform the following data preparation steps using Snowpark SQLAPI:

A) Creating a feature that returns 1 if there is a PROMOTION_FLAG of True and SALES_AMOUNT > 1000, and zero otherwise
B) Converting 'SALE_DATE to a quarterly representation (e.g., '2023-QI').
C) Handling missing 'SALES_AMOUNT values by imputing them with the average 'SALES_AMOUNT' for the same 'PRODUCT_ID during the previous month. If there's no data for the previous month, use the overall average for that
D) All of the above.
E) Creating a new feature representing the percentage change in 'SALES_AMOUNT compared to the previous day for the same 'PRODUCT_ID. Handle the first day of each 'PRODUCT by setting 'SALES_GROWTH' to O.

5. You are performing exploratory data analysis on a dataset of customer transactions in Snowflake to prepare for a linear regression model that predicts transaction value based on several customer-related features (e.g., age, location, number of previous transactions). You suspect a non-linear relationship between 'customer_age' and 'transaction_value'. Which of the following Snowflake SQL techniques is MOST appropriate for exploring and potentially transforming the 'customer_age' variable to better fit a linear regression model?

A) Apply a logarithmic transformation to 'customer_age' if a scatter plot of 'customer_age' vs 'transaction_value' shows a curve that flattens out as 'customer_age' increases.
B) Create polynomial features by adding 'customer_ageA2' and 'customer_ageA3' as new columns to the table, without checking for interaction effects.
C) Implement a Box-Cox transformation in Snowpark Python, select a suitable transformation parameter based on the data, and apply the transformation on 'customer_age' feature.
D) Use the window function to bin 'customer_age' into quartiles and treat each quartile as a categorical variable in the linear regression model.
E) Calculate the Pearson correlation coefficient between 'customer_age' and 'transaction_value' using the function. If the correlation is low, discard the 'customer_age' variable.

Solutions: