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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) 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.
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) 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) 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.

2. You have a Snowflake Model Registry set up and are managing multiple versions of a machine learning model. You want to programmatically retrieve a specific version of the model and load it for inference within a Snowflake Snowpark Python UDE Assume your registry name is 'my_registry', the model name is 'credit risk_model', and you want to retrieve version 'v2'. How would you achieve this using Snowpark Python?

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

3. You are developing a Spark application that needs to read data from a Snowflake table and write the processed data back to a different Snowflake table. Which of the following configurations and code snippets, used in conjunction with the Spark Snowflake Connector, would ensure secure and efficient data transfer, taking into account potential network latency and authentication best practices? Select all that apply.

A) Set 'sfURL', 'sfUser', 'sfPassword', 'sfDatabase', and 'sfSchema' properties in the Spark configuration. Use to read data and to write data. Rely on Snowflake's default JDBC driver settings for network optimization.
B) Employ Snowflake's OAuth authentication. Obtain an OAuth token and pass it as a parameter to the Spark Snowflake Connector. You need to also provide 'sfDatabase' and 'sfSchemas properties. Don't set the number of partitions, leaving it to Spark's default behavior.
C)

D) Configure network timeout parameters in the Spark Snowflake Connector options to handle potential network latency, specifically 'networkTimeoutlnMilliSeconds'. Use 'PREACTIONS' and 'POSTACTIONS' to prepare and finalize data loading. Implement robust error handling to retry failed operations.
E) Use Snowflake's Key Pair Authentication. Store the private key securely, and configure 'sflJRL', 'sflJser', 'private_key', 'sfDatabase', and 'sfSchema' properties in the Spark configuration. Ensure the user has appropriate Snowflake privileges. Configure 'numPartitions' parameter based on the scale of data to parallelize read and write operations.

4. You are tasked with building a model to predict customer churn. You have a table named in Snowflake with the following relevant columns: 'customer_id', 'login_date', , 'orders_placed', , and 'churned' (binary indicator). You want to engineer features that capture customer engagement over time using Snowpark for Python. Which of the following feature engineering steps, applied sequentially, are MOST effective in creating features indicative of churn risk?

A) 1. Calculate the maximum 'page_views' in a single day for each customer. 2. Calculate the total number of days with no 'login_date' for each customer. 3. Create a feature indicating if a customer has ever placed an order. 4. Use a simple boolean for the 'subscription_type' column.
B) 1. Calculate the average 'page_views' per day for each customer. 2. Calculate the total number of for each customer. 3. Create a feature indicating whether the customer has a premium subscription ('subscription_type' = 'premium').
C) 1. Calculate the average 'page_views' per week for each customer over the last 3 months using a window function. 2. Calculate the recency of the last order (days since last order) for each customer. 3. Create a feature indicating the change in average daily page views over the last month compared to the previous month. 4. Create a feature showing standard deviation of page_views per customer over the last 90 days.
D) 1. Calculate the number of days since the customer's last login, and use nulls instead of negative numbers to indicate inactivity. 2. Calculate the rolling 7-day average of 'orders_placed' using a window function, partitioning by 'customer_id' and ordering by 'login_date'. 3. Calculate the slope of a linear regression of page_views' over time for each customer, indicating the trend in engagement using Snowpark ML. 4. Calculate the percentage of weeks the customer logged in. 5. Create a feature showing standard deviation of page_views per customer over the last 90 days.
E) 1. Calculate the total 'page_views' and 'orders_placed' for each customer without considering time. 2. Use one-hot encoding for the 'subscription_type' column.

5. You're developing a fraud detection system in Snowflake. You're using Snowflake Cortex to generate embeddings from transaction descriptions, aiming to cluster similar fraudulent transactions. Which of the following approaches are MOST effective for optimizing the performance and cost of generating embeddings for a large dataset of millions of transaction descriptions using Snowflake Cortex, especially considering the potential cost implications of generating embeddings at scale? Select two options.

A) Implement caching mechanism based on a hash of transaction description if transaction description does not change then no need to recompute the emebeddings again.
B) Generate embeddings on the entire dataset every day to capture all potential fraudulent transactions and ensure the model is always up-to-date.
C) Use a Snowflake Task to incrementally generate embeddings only for new transactions that have been added since the last embedding generation run.
D) Create a materialized view containing pre-computed embeddings for all transaction descriptions.
E) Generate embeddings using snowflake-cortex-embed-text function, using the OPENAI embedding model

Solutions: