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Snowflake SnowPro Advanced: Data Scientist Certification Sample Questions:

1. You are tasked with predicting the sales price of houses based on their size (square footage) using linear regression in Snowflake. You have a table named 'HOUSE PRICES' with columns 'SQUARE FOOTAGE' and 'SALES PRICE'. You want to calculate the slope and intercept using Snowflake SQL. Which of the following queries, considering potential NULL values in the data, is the MOST robust and statistically sound for calculating the slope and intercept for a simple linear regression model?

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

2. You are training a binary classification model in Snowflake using Snowpark to predict customer churn. The dataset contains a mix of numerical and categorical features, and you've identified that the 'COUNTRY' feature has high cardinality. You observe that your model performs poorly for less frequent countries. To address this, you decide to up-sample the minority classes within the 'COUNTRY' feature before training. Which combination of techniques would be MOST appropriate and computationally efficient for up-sampling in this scenario within Snowflake, considering you are working with a large dataset and want to minimize data shuffling across the network?

A) Use a stored procedure written in Python to iterate through each unique country, identify minority countries, and then use Snowpark to up-sample those countries using 'DataFrame.sample()' with replacement. This offers the most flexibility but introduces significant overhead due to context switching.
B) Use Snowpark's 'DataFrame.groupBy()" and 'DataFrame.count()' to identify minority countries. Then, for each minority country, use DataFrame.unionByName()' to combine the original data with multiple copies of the minority country's data, created using 'DataFrame.sample()' with replacement. This minimizes data movement within Snowflake.
C) Use the 'SAMPLE clause in Snowflake SQL with 'REPLACE' for each minority country, creating separate temporary tables and then combining them with UNION ALL'. This is efficient for small datasets but scales poorly with high cardinality.
D) Leverage Snowpark's 'DataFrame.collect()' to bring the entire dataset to the client machine, then use Python's scikit-learn library for up-sampling. This is suitable only for small datasets as it incurs significant network overhead.
E) Utilize Snowflake UDFs (User-Defined Functions) written in Java to perform stratified sampling on the 'COUNTRY' feature, ensuring each minority class is adequately represented in the up-sampled dataset. UDFs allow for complex logic but can be challenging to debug within Snowflake.

3. You are building a predictive model on customer churn using Snowflake data'. You observe that the distribution of 'TIME SINCE LAST PURCHASE' is heavily left-skewed. Which of the following strategies would be MOST appropriate to handle this skewness before feeding the data into a linear regression model to improve its performance? (Select TWO)

A) Use a winsorization technique to cap extreme values in the 'TIME SINCE LAST PURCHASE' column at a predefined percentile (e.g., 99th percentile).
B) Apply a square root transformation to the 'TIME_SINCE_LAST_PURCHASE' column.
C) Remove all records with 'TIME SINCE LAST PURCHASE' values below the mean.
D) Apply a logarithmic transformation to the 'TIME SINCE LAST PURCHASE' column.
E) Standardize the 'TIME_SINCE_LAST_PURCHASE' column using Z-score normalization.

4. 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) The 'contract_length' feature is the most important feature for the model's predictive performance; shuffling it causes the largest drop in AUC.
B) 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.
C) The 'contract_length' and 'monthly_charges' features are equally important.
D) 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.
E) The 'support_calls' feature is the least important feature; removing it entirely from the model will have little impact on its AUC performance.

5. You are tasked with training a logistic regression model in Snowflake using Snowpark Python to predict customer churn. Your data is stored in a table named 'CUSTOMER DATA' with columns like 'CUSTOMER D', 'FEATURE 1', 'FEATURE 2', 'FEATURE 3', and 'CHURN FLAG' (boolean representing churn). You plan to use stratified k-fold cross-validation to ensure each fold has a representative proportion of churned and non-churned customers. Which of the following code snippets demonstrates the correct way to perform stratified k-fold cross-validation with Snowpark ML? (Assume 'snowpark_session' is a valid Snowpark session object).

A)

B)

C)

D)

E)

Solutions: