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Snowflake SnowPro Advanced: Data Scientist Certification Sample Questions:

1. A financial institution is analyzing transaction data in Snowflake to detect fraudulent activity. They have a 'Transaction_Amount' column. They want to binarize this feature, creating a new 'ls_High_Value' column. Transactions with amounts greater than $1000 should be marked as 1 (High Value), and all other transactions (including NULLs) should be marked as 0. Which of the following SQL statements would be the MOST efficient and correct way to achieve this in Snowflake?

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

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 'monthly_charges' feature has no impact on the customer's churn probability.
B) 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.
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. A data scientist is using association rule mining with the Apriori algorithm on customer purchase data in Snowflake to identify product bundles. After generating the rules, they obtain the following metrics for a specific rule: Support = 0.05, Confidence = 0.7, Lift = 1.2. Consider that the overall purchase probability of the consequent (right-hand side) of the rule is 0.4. Which of the following statements are CORRECT interpretations of these metrics in the context of business recommendations for product bundling?

A) The rule applies to 5% of all transactions in the dataset, meaning 5% of the transactions contain both the antecedent and the consequent.
B) The lift value of 1.2 suggests a strong negative correlation between the antecedent and consequent, indicating that purchasing the antecedent items decreases the likelihood of purchasing the consequent items.
C) The confidence of 0.7 indicates that 70% of transactions containing the antecedent also contain the consequent.
D) The lift value of 1.2 indicates that customers are 20% more likely to purchase the consequent items when they have also purchased the antecedent items, compared to the baseline purchase probability of the consequent items.
E) Customers who purchase the items in the antecedent are 70% more likely to also purchase the items in the consequent, compared to the overall purchase probability of the consequent.

4. A data scientist is building a linear regression model in Snowflake to predict customer churn based on structured data stored in a table named 'CUSTOMER DATA'. The table includes features like 'CUSTOMER D', 'AGE, 'TENURE MONTHS', 'NUM PRODUCTS', and 'AVG MONTHLY SPEND'. The target variable is 'CHURNED' (1 for churned, 0 for active). After building the model, the data scientist wants to evaluate its performance using Mean Squared Error (MSE) on a held-out test set. Which of the following SQL queries, executed within Snowflake's stored procedure framework, is the MOST efficient and accurate way to calculate the MSE for the linear regression model predictions against the actual 'CHURNED values in the 'CUSTOMER DATA TEST table, assuming the linear regression model is named 'churn _ model' and the predicted values are generated by the MODEL APPLY() function?

A)

B)

C)

D)

E)

5. You are investigating website session durations stored in a Snowflake table named 'WEB SESSIONS. You suspect that bot traffic is artificially inflating the average session duration. You have the following session durations (in seconds) in the 'SESSION DURATION' column: [10, 12, 15, 18, 20, 22, 25, 28, 30, 1000]. Given this data and the context of bot traffic, which measure of central tendency is MOST robust to the influence of the outlier (1000) in this dataset? Assuming you already have table and dataframe created for this analysis. (Choose ONE)

A) Trimmed mean (e.g. 10% trimmed)
B) Median
C) Mean
D) Geometric Mean
E) Mode

Solutions: