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Snowflake SnowPro Advanced: Data Scientist Certification Sample Questions:

Question 1

You are tasked with deploying a fraud detection model in Snowflake using the Model Registry. The model is trained on a dataset that is updated daily. You need to ensure that your deployed model uses the latest approved version and that you can easily roll back to a previous version if any issues arise. Which of the following approaches would provide the most robust and maintainable solution for model versioning and deployment, considering minimal downtime during updates and rollback?

A. Create multiple Snowflake UDFs, each corresponding to a different model version. Manually switch the active UDF by updating application code when a new model is deployed.
B. Store all model versions within a single model registry entry without versioning, overwriting the existing file with each new training run.
C. Deploy a new Snowflake UDF referencing the model file directly in cloud storage every time the model is retrained. Rely on cloud storage versioning for rollback.
D. Register each new model version in the Snowflake Model Registry and promote the desired version to 'PRODUCTION' stage. Update a single UDF that dynamically fetches the model based on the 'PRODUCTION' stage metadata.
E. Use Snowflake Tasks to periodically refresh a table containing the latest model weights. The UDF directly queries this table for predictions.

Question 2

You are training a binary classification model in Snowflake to predict customer churn using Snowpark Python. The dataset is highly imbalanced, with only 5% of customers churning. You have tried using accuracy as the optimization metric, but the model performs poorly on the minority class. Which of the following optimization metrics would be most appropriate to prioritize for this scenario, considering the imbalanced nature of the data and the need to correctly identify churned customers, along with a justification for your choice?

A. Area Under the Receiver Operating Characteristic Curve (AUC-ROC) - as it measures the ability of the model to distinguish between the two classes, irrespective of the class distribution.
B. Log Loss (Binary Cross-Entropy) - as it penalizes incorrect predictions proportionally to the confidence of the prediction, suitable for probabilistic outputs.
C. Accuracy - as it measures the overall correctness of the model.
D. Root Mean Squared Error (RMSE) - as it is commonly used for regression problems, not classification.
E. F 1-Score - as it balances precision and recall, providing a good measure for imbalanced datasets.

Question 3

You are developing a fraud detection model in Snowflake using Snowpark Python. You've iterated through multiple versions of the model, each with different feature sets and algorithms. To ensure reproducibility and easy rollback in case of performance degradation, how should you implement model versioning within your Snowflake environment, focusing on the lifecycle step of Deployment & Monitoring?

A. Store the trained models directly in external cloud storage (e.g., AWS S3, Azure Blob Storage) with explicit versioning enabled on the storage layer, and update Snowflake metadata (e.g., in a table) to point to the current model version. Use a UDF to load the correct model version.
B. Store each model version as a separate Snowflake table, containing serialized model objects and metadata like training date, feature set, and performance metrics. Use views to point to the 'active' version.
C. Utilize Snowflake's Time Travel feature to revert to previous versions of the model artifact stored in a Snowflake stage.
D. Only maintain the current model version. If any problems arise, retrain a new model and redeploy it to replace the faulty one.
E. Implement a custom versioning system using Snowflake stored procedures that track model versions and automatically deploy the latest model by overwriting the existing one. The prior version gets deleted.

Question 4

A data scientist uses bootstrapping to estimate the sampling distribution of a statistic calculated from a dataset stored in Snowflake. They observe that the bootstrap distribution is significantly different from the original data distribution. Which of the following statements best describes the possible reasons for this difference, considering both the theoretical underpinnings of bootstrapping and potential limitations?

A. Bootstrapping is only appropriate for normally distributed data; if the original data is not normal, the bootstrap distribution will inevitably differ significantly.
B. The statistic being estimated is inherently unstable and has a high variance, causing the bootstrap distribution to be wider and potentially different in shape compared to the original data distribution. This is a normal outcome when dealing with such statistics.
C. The original sample may not be representative of the population, and the bootstrap procedure is simply amplifying the biases present in the original sample. Additionally, the statistic itself may be highly sensitive to outliers or specific data points, leading to a distorted bootstrap distribution.
D. Bootstrapping always provides accurate estimates of sampling distributions, any significant difference indicates an error in the code implementation.
E. The difference is unexpected; the bootstrap distribution should always closely resemble the original data distribution, regardless of the statistic being estimated.

Question 5

You are tasked with building a machine learning pipeline in Snowpark Python to predict customer lifetime value (CLTV). You need to access and manipulate data residing in multiple Snowflake tables and views, including customer demographics, purchase history, and website activity. To improve code readability and maintainability, you decide to encapsulate data access and transformation logic within a Snowpark Stored Procedure. Given the following Python code snippet representing a simplified version of your stored procedure:

A. The 'snowflake.snowpark.context.get_active_session()' function retrieves the active Snowpark session object, enabling interaction with the Snowflake database from within the stored procedure.
B. The 'session.table('CUSTOMER DEMOGRAPHICS')' method creates a local Pandas DataFrame containing a copy of the data from the 'CUSTOMER DEMOGRAPHICS' table.
C. The 'session.sql('SELECT FROM PURCHASE line executes a SQL query against the Snowflake database and returns the results as a list of Row objects.
D. The replace=True, packages=['snowflake-snowpark-python', 'pandas', decorator registers the Python function as a Snowpark Stored Procedure, allowing it to be called from SQL.
E. The 'session.write_pandas(df, table_name='CLTV PREDICTIONS', auto_create_table=Truey function writes the Pandas DataFrame 'df containing the CLTV predictions directly to a new Snowflake table named , automatically creating the table if it does not exist.

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