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Snowflake SnowPro Advanced: Data Scientist Certification Sample Questions:

1. You are a data scientist working for a retail company that stores its transaction data in Snowflake. You need to perform feature engineering on customer purchase history data to build a customer churn prediction model. Which of the following approaches best combines Snowflake's capabilities with a machine learning framework (like scikit-learn) for efficient feature engineering? Assume your data is stored in a table named 'CUSTOMER TRANSACTIONS' with columns like 'CUSTOMER ID, 'TRANSACTION DATE, 'AMOUNT, and 'PRODUCT CATEGORY.

A) Extract all the data from 'CUSTOMER_TRANSACTIONS' into a Pandas DataFrame, perform feature engineering using Pandas and scikit-learn, and then load the processed data back into Snowflake.
B) Develop a custom Spark application to read data from Snowflake, perform feature engineering in Spark, and write the resulting features back to a new table in Snowflake, and avoid use of Snowflake SQL UDFs to minimize complexity.
C) Use Snowflake's SQL UDFs (User-Defined Functions) written in Python to perform feature engineering directly within Snowflake on smaller aggregated sets of data to optimize compute costs. Integrate these UDFs to query the entire 'CUSTOMER TRANSACTIONS table to build your features.
D) Load a small subset of 'CUSTOMER_TRANSACTIONS' into an in-memory database like Redis, perform feature engineering using custom Python scripts interacting with Redis, and periodically sync the results back to Snowflake.
E) Create a Snowflake external function that calls a cloud-based (AWS, Azure, GCP) machine learning service for feature engineering, passing the raw transaction data for each customer and processing the aggregated data into features in Snowflake SQL.

2. You are deploying a pre-trained image classification model stored as a serialized file in an internal stage within Snowflake. You need to create a UDF to load this model and use it for inference on image data stored in a VARIANT column. The model was trained using Python's scikit-learn library and uses OpenCV for image processing. Which of the following code snippets correctly outlines the steps required to create and deploy this UDF? Assume you have already created an internal stage named 'MODEL STAGE and uploaded the model file into it. You also need to create a temporary directory that will be removed after the execution.

A)

B)

C)

D)

E)

3. 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) Use Snowflake Tasks to periodically refresh a table containing the latest model weights. The UDF directly queries this table for predictions.
B) 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.
C) 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.
D) 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.
E) Store all model versions within a single model registry entry without versioning, overwriting the existing file with each new training run.

4. 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) F 1-Score - as it balances precision and recall, providing a good measure for imbalanced datasets.
B) Accuracy - as it measures the overall correctness of the model.
C) Root Mean Squared Error (RMSE) - as it is commonly used for regression problems, not classification.
D) 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.
E) Log Loss (Binary Cross-Entropy) - as it penalizes incorrect predictions proportionally to the confidence of the prediction, suitable for probabilistic outputs.

5. You are tasked with training a logistic regression model in Snowflake using Snowpark Python to predict customer churn. Your data is stored in a table named 'CUSTOMER DATA' with columns like 'CUSTOMER D', 'FEATURE 1', 'FEATURE 2', 'FEATURE 3', and 'CHURN FLAG' (boolean representing churn). You plan to use stratified k-fold cross-validation to ensure each fold has a representative proportion of churned and non-churned customers. Which of the following code snippets demonstrates the correct way to perform stratified k-fold cross-validation with Snowpark ML? (Assume 'snowpark_session' is a valid Snowpark session object).

A)

B)

C)

D)

E)

Solutions: