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Snowflake SnowPro Advanced: Data Scientist Certification Sample Questions:

1. A financial institution aims to detect fraudulent transactions using a Supervised Learning model deployed in Snowflake. They have a dataset with transaction details, including amount, timestamp, merchant category, and customer ID. The target variable is 'is_fraudulent' (0 or 1). They are considering different Supervised Learning algorithms. Which of the following algorithms would be MOST suitable for this fraud detection task, considering the need for interpretability, scalability, and the potential for imbalanced classes, and what specific strategies can be employed within Snowflake to handle the class imbalance?

A) Naive Bayes, because it requires no hyperparameter tuning and works well on numerical data.
B) Decision Tree or Random Forest, combined with techniques like oversampling the minority class (fraudulent transactions) within Snowflake using SQL or UDFs to balance the dataset before training. These models provide reasonable interpretability and can handle non-linear relationships effectively.
C) Support Vector Machine (SVM) with a radial basis function (RBF) kernel, as it can capture complex non-linear relationships without concern for interpretability.
D) K-Nearest Neighbors (KNN), because it is simple to implement and doesn't require extensive training.
E) Linear Regression, because it's computationally efficient and easy to understand, even though fraud detection is a classification problem.

2. You are working with a Snowflake table named 'CUSTOMER DATA' containing customer information, including a 'PHONE NUMBER' column. Due to data entry errors, some phone numbers are stored as NULL, while others are present but in various inconsistent formats (e.g., with or without hyphens, parentheses, or country codes). You want to standardize the 'PHONE NUMBER column and replace missing values using Snowpark for Python. You have already created a Snowpark DataFrame called 'customer df representing the 'CUSTOMER DATA' table. Which of the following approaches, used in combination, would be MOST efficient and reliable for both cleaning the existing data and handling future data ingestion, given the need for scalability?

A) Use a series of and methods on the Snowpark DataFrame to handle NULL values and different phone number formats directly within the DataFrame operations.
B) Use a UDF (User-Defined Function) written in Python that formats the phone numbers based on a regular expression and applies it to the DataFrame using For NULL values, replace them with a default value of 'UNKNOWN'.
C) Leverage Snowflake's data masking policies to mask any invalid phone number and create a view that replaces NULL values with 'UNKNOWN'. This approach doesn't correct existing data but hides the issue.
D) Create a Snowflake Stored Procedure in SQL that uses regular expressions and 'CASE statements to format the "PHONE_NUMBER column and replace NULL values. Call this stored procedure from a Snowpark Python script.
E) Create a Snowflake Pipe with a COPY INTO statement and a transformation that uses a SQL function within the COPY INTO statement to format the phone numbers and replace NULL values during data loading. Also, implement a Python UDF for correcting already existing data.

3. You are working with a large dataset in Snowflake and need to build a machine learning model using scikit-learn in Python. You want to leverage Snowflake's compute resources for feature engineering to speed up the process. Which of the following approaches correctly combines Snowflake's SQL capabilities with scikit-learn for feature engineering and model training, while minimizing data transfer between Snowflake and the Python environment?

A) Write a complex SQL query in Snowmake to perform all feature engineering, then load the resulting features into a Pandas DataFrame and train the scikit-learn model.
B) Use Snowflake external functions to invoke a remote service (e.g., AWS Lambda) for feature engineering. Pass data from Snowflake to the remote service, receive the engineered features back, and load them into a Pandas DataFrame for model training.
C) Implement the feature engineering steps directly in Python using Pandas and scikit-learn, then load the raw data into a Pandas DataFrame and apply the transformations. Finally, train the scikit-learn model.
D) Create Snowflake User-Defined Functions (UDFs) in Python for complex feature engineering calculations. Call these UDFs within a SQL query to apply the feature engineering to the Snowflake data. Load the resulting features into a Pandas DataFrame and train the scikit-learn model.
E) Use the Snowflake Python Connector to execute individual SQL queries for each feature engineering step. Load the resulting features step-by-step into a Pandas DataFrame and train the scikit-learn model.

4. You are building a customer churn prediction model in Snowflake using Snowflake ML. After training, you need to evaluate the model's performance and identify areas for improvement. Given the following table 'PREDICTIONS' contains predicted probabilities and actual churn labels, which SQL query effectively calculates both precision and recall for the churn class (where 'CHURN = 1')?

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

5. You've deployed a regression model in Snowflake to predict product sales. After a month, you observe that the RMSE on your validation dataset has increased significantly compared to the initial deployment. Analyzing the prediction errors, you notice a pattern: the model consistently underestimates sales for products with a recent surge in social media mentions. Which of the following actions would be MOST effective in addressing this issue and improving the model's RMSE?

A) Decrease the learning rate of the optimization algorithm during retraining to avoid overshooting the optimal weights.
B) Retrain the model using only the most recent data (e.g., last week) to adapt to the changing sales patterns.
C) Increase the regularization strength of the model to prevent overfitting to the original training data.
D) Implement a moving average smoothing technique on the target variable (sales) before retraining the model.
E) Incorporate a feature representing the number of social media mentions for each product into the model and retrain.

Solutions: