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Snowflake SnowPro Advanced: Data Scientist Certification Sample Questions:

1. You are tasked with training a complex machine learning model using scikit-learn and need to leverage Snowflake's data for training outside of Snowflake using an external function. The training data resides in a Snowflake table named 'CUSTOMER DATA'. Due to data governance policies, you must ensure minimal data movement and secure communication. You choose to implement the external function using AWS Lambda'. Which of the following steps are crucial to achieve secure and efficient model training outside of Snowflake?

A) Utilize Snowflake's data masking policies on the table to anonymize sensitive information before sending it to the external function for training. This ensures data privacy and compliance with regulations.
B) Grant usage privilege on the API integration object to the role that will be calling the external function, ensuring only authorized users can trigger the model training.
C) Create an external function in Snowflake that accepts a JSON payload containing the necessary parameters for model training, such as features to use and model hyperparameters. This function will call the API integration to invoke the Lambda function.
D) In the Lambda function, establish a direct connection to the Snowflake database using the Snowflake JDBC driver and Snowflake user credentials stored in the Lambda environment variables. This allows the Lambda function to directly query the 'CUSTOMER DATA' table.
E) Create an API integration object in Snowflake that points to your AWS API Gateway endpoint, configured to invoke the Lambda function. This API integration must use a service principal and access roles for secure authentication.

2. You are building a machine learning model to predict customer churn for a telecommunications company. One of the features is 'tariff_plan', which is a string representing different tariff plans (e.g., 'Basic', 'Premium', 'Unlimited'). You need to encode this feature for your model, but you also want to handle potential new tariff plans that might appear in future data'. Which encoding method and Snowflake SQL approach would be MOST suitable to minimize dimensionality and address unseen values effectively, assuming the number of plans is moderately high (around 20-30)?

A) Hash Encoding (Feature Hashing) using a UDF in Snowflake, with a fixed number of features and a hashing function to map each tariff plan to a feature index, accepting potential collisions. Handle new tariff plans naturally through the hashing function.
B) One-Hot Encoding using CREATE OR REPLACE VIEW, handling new values by NULLIF('Unknown', tariff_plan) before encoding, potentially leading to a high number of columns.
C) Binary Encoding using a UDF to convert each tariff plan into binary code, storing encoded results into snowflake, then splitting the binary representation into separate columns.
D) Label Encoding using a UDF (User-Defined Function) with a predefined mapping, assigning a new integer to unseen values, and storing the mapping in a separate table in Snowflake.
E) Target Encoding (Mean Encoding) using Snowflake SQL, calculating the mean churn rate for each tariff plan and using that as the encoded value. Handle unseen values with the global mean churn rate, being mindful of potential target leakage.

3. You have trained a logistic regression model in Python using scikit-learn and plan to deploy it as a Python stored procedure in Snowflake. You need to serialize the model for deployment. Consider the following code snippet:

A) The code will fail because it does not handle potential security vulnerabilities associated with deserializing pickled objects from untrusted sources.
B) The code will fail because Snowflake stages cannot be used to store model objects.
C) The code will fail because the 'model_bytes' variable is not accessible within the 'predict' function's scope.
D) The code will execute successfully. The model serialization and deserialization using pickle are correctly implemented within the stored procedure.
E)

4. You are using Snowpark Pandas to prepare data for a machine learning model. You have a Snowpark DataFrame named 'transactions df that contains transaction data, including 'transaction id', 'product id', 'customer id', and 'transaction_amount'. You want to create a new feature that represents the average transaction amount per customer. However, you are concerned about potential skewness in the 'transaction_amount' and want to apply a log transformation to reduce its impact before calculating the average. Which of the following steps using Snowpark Pandas would achieve this transformation and calculation most efficiently within Snowflake?

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

5. You are deploying a time series forecasting model in Snowflake. You need to log the performance metrics (e.g., MAE, RMSE) of the model after each prediction run to the Snowflake Model Registry. Which of the following steps are necessary to achieve this?

A) Use the method to log individual metrics to the Model Registry associated with a specific model version after the prediction run.
B) Create a separate table in Snowflake to store the performance metrics and use SQL "INSERT statements to log the metrics after each prediction run.
C) Leverage Snowflake's Event Tables to capture and store metrics data generated during model evaluation and prediction workflows and then access via stored procedures that log to the Model Registry.
D) Use the method with the 'metrics' parameter to log the metrics directly during model registration.
E) You must create a custom logging solution outside of Snowflake using external services and then integrate those logs back into Snowflake via external functions and Model Registry APIs

Solutions: