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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) 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.
B) 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.
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) 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.
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 a data scientist working for a retail company. You've been tasked with identifying fraudulent transactions. You have a Snowflake table named 'TRANSACTIONS' with columns 'TRANSACTION ID', 'AMOUNT', 'TRANSACTION DATE', 'CUSTOMER ID', and 'LOCATION'. You suspect outliers in transaction amounts might indicate fraud. Which of the following SQL queries is the MOST efficient and appropriate to identify potential outliers using the Interquartile Range (IQR) method, and incorporate necessary data type considerations for robust percentile calculations? Consider also the computational cost associated with each approach on a large dataset.

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

3. You are validating a time series forecasting model for daily sales using Snowflake and Snowpark. The residuals plot shows a clear sinusoidal pattern. Which of the following actions should you consider to improve your model? (Select all that apply)

A) Change the algorithm to a linear regression model, since it is more likely to capture sinusoidal patterns
B) Incorporate lagged features representing previous sales values (e.g., sales from the previous day, week, or month).
C) Increase the regularization strength in your model.
D) Remove outlier data points to improve overall model performance.
E) Apply a Box-Cox transformation to the target variable (sales) to stabilize the variance.

4. A data engineer is tasked with removing duplicates from a table named 'USER ACTIVITY' in Snowflake, which contains user activity logs. The table has columns: 'ACTIVITY TIMESTAMP', 'ACTIVITY TYPE', and 'DEVICE_ID. The data engineer wants to remove duplicate rows, considering only 'USER ID', 'ACTIVITY TYPE, and 'DEVICE_ID' columns. What is the most efficient and correct SQL query to achieve this while retaining only the earliest 'ACTIVITY TIMESTAMP' for each unique combination of the specified columns?

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

5. You have deployed a fraud detection model in Snowflake that predicts the probability of a transaction being fraudulent. After a month, you observe that the model's precision has significantly dropped. You suspect data drift. Which of the following actions would be MOST effective in identifying and quantifying the data drift in Snowflake, assuming you have access to the transaction data before and after deployment?

A) Calculate the Jensen-Shannon Divergence between the probability distributions of predicted fraud scores on the training set and the current production data set.
B) Retrain the model daily with the most recent transaction data without performing any explicit data drift analysis, relying on the model to adapt to the changes.
C) Periodically sample a small subset of the recent transaction data and manually compare it with the training data using descriptive statistics (mean, standard deviation).
D) Use Snowflake's built-in profiling capabilities to generate summary statistics for the training data. Compare these summary statistics with the statistics generated for recent transaction data. If significant differences are observed, assume data drift.
E) Create a UDF in Snowflake to calculate the Kolmogorov-Smirnov (KS) statistic for each feature between the training data and the recent transaction data. Then, create an alert if the KS statistic exceeds a predefined threshold for any feature.

Solutions: