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Snowflake SnowPro Advanced: Data Scientist Certification Sample Questions:

1. A data scientist is preparing customer churn data for a machine learning model in Snowflake. The dataset contains a 'Contract_Type' column with values 'Month-to-Month', 'One Year', and 'Two Year'. They want to use label encoding to transform this categorical feature into numerical values. Which of the following SQL statements correctly implements label encoding for the 'Contract_Type' column, assigning 'Month-to-Month' to 0, 'One Year' to 1, and 'Two Year' to 2, and creates a new column named 'Contract_Type_Encoded'? Additionally, the data scientist wants to handle potential NULL values in 'Contract_Type' by assigning them the value of -1.

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

2. A data scientist is performing exploratory data analysis on a table named 'CUSTOMER TRANSACTIONS. They need to calculate the standard deviation of transaction amounts C TRANSACTION AMOUNT) for different customer segments CCUSTOMER SEGMENT). The 'CUSTOMER SEGMENT column can contain NULL values. Which of the following SQL statements will correctly compute the standard deviation, excluding NULL transaction amounts, and handling NULL customer segments by treating them as a separate segment called 'Unknown'? Consider using Snowflake-specific functions where appropriate.

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

3. You are tasked with deploying a time series forecasting model within Snowflake using Snowpark Python. The model requires significant pre-processing and feature engineering steps that are computationally intensive. These steps include calculating rolling statistics, handling missing values with imputation, and applying various transformations. You aim to optimize the execution time of these pre- processing steps within the Snowpark environment. Which of the following techniques can significantly improve the performance of your data preparation pipeline?

A) Force single-threaded execution by setting to avoid overhead associated with parallel processing.
B) Utilize Snowpark's vectorized UDFs and DataFrame operations to leverage Snowflake's distributed computing capabilities.
C) Ensure that all data used is small enough to fit within the memory of the client machine running the Snowpark Python script, thus removing the need for distributed computing.
D) Write the feature engineering logic directly in SQL and create a view. Use the Snowpark DataFrame API to query the view, avoiding Python code execution within Snowpark.
E) Convert the Snowpark DataFrame to a Pandas DataFrame using and perform all pre-processing operations using Pandas functions before loading the processed data back to Snowflake.

4. 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) 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.
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) 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.
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.

5. You have a Snowpark DataFrame named 'product_reviews' containing customer reviews for different products. The DataFrame includes columns like 'product_id' , 'review_text' , and 'rating'. You want to perform sentiment analysis on the 'review_text' to identify the overall sentiment towards each product. You decide to use Snowpark for Python to create a user-defined function (UDF) that utilizes a pre-trained sentiment analysis model hosted externally. You need to ensure secure access to this model and efficient execution. Which of the following represents the BEST approach, considering security and performance?

A) Create an external function in Snowflake that calls a serverless function (e.g., AWS Lambda, Azure Function) that performs the sentiment analysis. Use Snowflake's network policies to restrict access to the serverless function and secrets management to handle API keys.
B) Create an inline Python UDF that directly calls the external sentiment analysis API with hardcoded API keys within the UDF code.
C) Create a Java UDF that utilizes a library to call the sentiment analysis API. Pass the API key as a parameter to the UDF each time it is called.
D) Create an external function in Snowflake that calls a serverless function. Configure the API gateway in front of the serverless function to enforce authentication via Mutual TLS (mTLS) using Snowflake-managed certificates.
E) Create a Snowpark Pandas UDF that calls the external sentiment analysis API. Use Snowflake secrets management to store the API key and retrieve it within the UDF.

Solutions: