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Snowflake DSA-C03 Exam Syllabus Topics:

Snowflake SnowPro Advanced: Data Scientist Certification Sample Questions:

1. 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)

2. You are tasked with predicting sales (SALES AMOUNT') for a retail company using linear regression in Snowflake. The dataset includes features like 'ADVERTISING SPEND', 'PROMOTIONS', 'SEASONALITY INDEX', and 'COMPETITOR PRICE'. After training a linear regression model named 'sales model', you observe that the model performs poorly on new data, indicating potential issues with multicollinearity or overfitting. Which of the following strategies, applied directly within Snowflake, would be MOST effective in addressing these issues and improving the model's generalization performance? Choose ALL that apply.

A) Apply Ridge Regression by adding an L2 regularization term during model training. This can be achieved by setting the 'REGULARIZATION' parameter of the 'CREATE MODEL' statement to 'L2'.
B) Perform feature scaling (e.g., standardization or min-max scaling) on the input features before training the model, using Snowflake's built-in functions or user-defined functions (UDFs) for scaling.
C) Decrease the 'MAX_ITERATIONS' parameter in the 'CREATE MODEL' statement to prevent the model from overfitting to the training data.
D) Increase the size of the training dataset significantly by querying data from external sources.
E) Manually remove highly correlated features (e.g., if 'ADVERTISING SPEND and 'PROMOTIONS' have a correlation coefficient above 0.8) based on a correlation matrix calculated using 'CORR function and feature selection techniques.

3. You have trained a linear regression model in Snowpark ML to predict house prices. After training, you want to assess the overall feature importance using the model's coefficients. Consider the following Snowflake table containing the coefficients:

Which of the following statements are correct interpretations of these coefficients regarding feature impact?

A) An increase of one square foot (sqft) in house size is associated with an increase of $120.5 in the predicted house price.
B) The 'location_score' feature is the most influential predictor in determining house price.
C) Increasing the number of bedrooms is associated with a decrease in the predicted house price.
D) The 'bedrooms' feature has a positive impact on the house price since the coefficient is negative.
E) The 'age' feature has an insignificant impact because its coefficient is small.

4. You are building an automated model retraining pipeline for a sales forecasting model in Snowflake using Snowflake Tasks and Stored Procedures. After retraining, you want to validate the new model against a champion model already deployed. You need to define a validation strategy using the following models: champion model deployed as UDF "FORECAST UDF , and contender model deployed as UDF 'FORECAST UDF NEW'. Given the following objectives: (1) Minimal impact on production latency, (2) Ability to compare predictions on a large volume of real-time data, (3) A statistically sound comparison metric. Which of the following SQL statements best represents how to efficiently compare the forecasts of the two models on a sample dataset and calculate the Root Mean Squared Error (RMSE) to validate the new model?

A)

B)

C)

D)

E)

5. You have trained a complex Random Forest model in Snowflake to predict loan default risk. You wish to understand the individual and combined effects of 'credit_score' and 'debt_to_income_ratio' on the predicted probability of default. Which approach is MOST suitable for visualizing and interpreting these relationships?

A) Calculate feature importance using SNOWFLAKE.ML.FEATURE IMPORTANCE and focus on the features with the highest scores.
B) Create a two-way Partial Dependence Plot (PDP) showing the interaction between 'credit_score' and 'debt_to_income_ratio'.
C) Examine the model's overall accuracy (e.g., AUC) and assume the relationships are well-represented.
D) Fit a simpler linear model (e.g., Logistic Regression) to the data and interpret its coefficients.
E) Generate individual Partial Dependence Plots (PDPs) for 'credit_score' and 'debt_to_income_ratio'.

Solutions: