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Snowflake SnowPro Advanced: Data Scientist Certification Sample Questions:

1. A data scientist is tasked with creating features for a machine learning model predicting customer churn. They have access to the following data in a Snowflake table named 'CUSTOMER ID, 'DATE, 'ACTIVITY _ TYPE' (e.g., 'login', 'purchase', 'support_ticket'), and 'ACTIVITY VALUE (e.g., amount spent, duration of login). Which of the following feature engineering strategies, leveraging Snowflake's capabilities, could be useful for predicting customer churn? (Select all that apply)

A) Create features that capture the trend of customer activity over time (e.g., increasing or decreasing activity) using LACY and 'LEAD' window functions.
B) Calculate the recency, frequency, and monetary value (RFM) for each customer using window functions and aggregate functions.
C) Create a feature representing the number of days since the customer's last login using "DATEDIFF and window functions.
D) Directly use the ACTIVITY TYPE column as a categorical feature without any transformation or engineering.
E) Use 'APPROX COUNT DISTINCT to estimate the number of unique product categories purchased by each customer within the last 3 months to create a features.

2. You are working on a customer churn prediction model and are using Snowpark Feature Store. One of your features, is updated daily. You notice that your model's performance degrades over time, likely due to stale feature values being used during inference. You want to ensure that the model always uses the most up-to-date feature values. Which of the following strategies would be the MOST effective way to address this issue using Snowpark Feature Store and avoid model staleness during online inference?

A) Implement a real-time feature retrieval service that directly queries the underlying Snowflake table containing the using Snowpark, bypassing the Feature Store.
B) Use the method on the Feature Store client during inference, ensuring that you always pass the current timestamp.
C) Configure with the attribute to manage data staleness and use the during inference, ensuring that the model always uses recent feature values.
D) Configure the Feature Group containing to automatically refresh every hour using a scheduled Snowpark Python function.
E) Define a custom User-Defined Function (UDF) in Snowflake that retrieves the 'customer_lifetime_value' from the Feature Store on demand whenever the model makes a prediction and set 'feature_retrieval_mode='fresh'S.

3. 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 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;
B) 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.
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) 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;
E) 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.

4. You are performing exploratory data analysis on a dataset containing customer transaction data in Snowflake. The dataset has a column named 'transaction_amount' and a column named 'customer_segment'. You want to analyze the distribution of transaction amounts for each customer segment using Snowflake's statistical functions. Which of the following approaches would BEST achieve this, providing insights into the central tendency and spread of the data?

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

5. A data scientist is exploring customer purchase data in Snowflake to identify high-value customer segments. They have a table named 'CUSTOMER TRANSACTIONS with columns 'CUSTOMER ID', 'TRANSACTION_DATE', and 'PURCHASE_AMOUNT'. They want to calculate the interquartile range (IQR) of 'PURCHASE AMOUNT for each customer. Which SQL query using Snowsight is the most efficient and accurate way to calculate and display the IQR for each 'CUSTOMER ID?

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

Solutions: