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Snowflake SnowPro Advanced: Data Scientist Certification Sample Questions:

1. You are preparing a dataset in Snowflake for a K-means clustering algorithm. The dataset includes features like 'age', 'income' (in USD), and 'number of_transactions'. 'Income' has significantly larger values than 'age' and 'number of_transactions'. To ensure that all features contribute equally to the distance calculations in K-means, which of the following scaling approaches should you consider, and why? Select all that apply:

A) Do not scale the data, as K-means is robust to differences in feature scales.
B) Apply PowerTransformer to transform income and StandardScaler to other features to handle skewness.
C) Apply StandardScaler to all three features ('age', 'income', 'number_of_transactions') to center the data around zero and scale it to unit variance.
D) Apply RobustScaler to handle outliers and then StandardScaler or MinMaxScaler to further scale the features.
E) Apply MinMaxScaler to all three features to scale them to a range between O and 1 .

2. You are working with a large sales transaction dataset in Snowflake, stored in a table named 'SALES DATA'. This table contains columns such as 'TRANSACTION_ID (unique identifier), 'CUSTOMER_ID', 'PRODUCT_ID, 'TRANSACTION_DATE' , and 'AMOUNT'. Due to a system error, some transactions were duplicated in the table. Your goal is to remove these duplicates efficiently using Snowpark for Python. You want to use the 'window.partitionBy()' and functions. Which of the following code snippets correctly removes duplicates based on all columns, while also creating a new column 'ROW NUM' to indicate the row number within each partition?

A)

B)

C)

D)

E)

3. You are tasked with building a predictive model in Snowflake to identify high-value customers based on their transaction history. The 'CUSTOMER_TRANSACTIONS table contains a 'TRANSACTION_AMOUNT column. You need to binarize this column, categorizing transactions as 'High Value' if the amount is above a dynamically calculated threshold (the 90th percentile of transaction amounts) and 'Low Value' otherwise. Which of the following Snowflake SQL queries correctly achieves this binarization, leveraging window functions for threshold calculation and resulting in a 'CUSTOMER SEGMENT column?

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

4. You've trained a binary classification model in Snowflake to predict loan defaults. You need to understand which features are most influential in the model's predictions for individual loans. Which of the following methods provide insight into model explainability, AND how can they be leveraged within the Snowflake environment? (Select all that apply)

A) LIME (Local Interpretable Model-agnostic Explanations): Can be implemented by creating a UDF (User-Defined Function) in Snowflake that takes a loan's feature values as input and returns the feature importance scores for that specific loan, based on the LIME algorithm applied to the model's predictions.
B) Permutation Feature Importance: Directly supported within Snowflake ML's model evaluation functions, allowing you to rank features based on their impact on model performance when their values are randomly shuffled.
C) Decision Tree visualization: Convert the model to decision trees and visualize it.
D) Coefficient analysis: By inspecting the coefficients of a linear model, we can easily determine feature importances.
E) SHAP (SHapley Additive explanations): Similar to LIME, SHAP values can be calculated using a Snowflake UDF, providing a more comprehensive and theoretically grounded explanation of each feature's contribution to the prediction, considering all possible feature combinations.

5. You are using a Snowflake Notebook to build a churn prediction model. You have engineered several features, and now you want to visualize the relationship between two key features: and , segmented by the target variable 'churned' (boolean). Your goal is to create an interactive scatter plot that allows you to explore the data points and identify any potential patterns.
Which of the following approaches is most appropriate and efficient for creating this visualization within a Snowflake Notebook?

A) Create a static scatter plot using Matplotlib directly within the Snowflake Notebook by converting the data to a Pandas DataFrame. This involves pulling all relevant data into the notebook's environment before plotting.
B) Use the 'snowflake-connector-python' to pull the data and use 'seaborn' to create static plots.
C) Leverage Snowflake's native support for Streamlit within the notebook to create an interactive application. Query the data directly from Snowflake within the Streamlit app and use Streamlit's plotting capabilities for visualization.
D) Use the Snowflake Connector for Python to fetch the data, then leverage a Python visualization library like Plotly or Bokeh to generate an interactive plot within the notebook.
E) Write a stored procedure in Snowflake that generates the visualization data in a specific format (e.g., JSON) and then use a JavaScript library within the notebook to render the visualization.

Solutions: