You are building a data science pipeline in Snowflake to predict customer churn. The pipeline involves extracting data, transforming it using Dynamic Tables, training a model using Snowpark ML, and deploying the model for inference. The raw data arrives in a Snowflake stage daily as Parquet files. You want to optimize the pipeline for cost and performance. Which of the following strategies are MOST effective, considering resource utilization and potential data staleness?

• A. Use a combination of Dynamic Tables for feature engineering and Snowpark ML for model training and deployment, ensuring proper dependency management and refresh intervals for each Dynamic Table based on data freshness requirements.
• B. Implement a series of smaller Dynamic Tables, each responsible for a specific transformation step, with well-defined refresh intervals tailored to the data's volatility and the downstream model's requirements.
• C. Schedule all data transformations and model training as a single large Snowpark Python script executed by a Snowflake task, ignoring data freshness requirements.
• D. Use a single, large Dynamic Table to perform all transformations in one step, relying on Snowflake's optimization to handle dependencies and incremental updates.
• E. Load all data into traditional Snowflake tables and use scheduled tasks with stored procedures written in Python to perform the transformations and model training.

A retail company is using Snowflake to store sales data'. They have a table called 'SALES DATA' with columns: 'SALE ID', 'PRODUCT D', 'SALE DATE', 'QUANTITY' , and 'PRICE'. The data scientist wants to analyze the trend of daily sales over the last year and visualize this trend in Snowsight to present to the business team. Which of the following approaches, using Snowsight and SQL, would be the most efficient and appropriate for visualizing the daily sales trend?

• A. Export all the data from the 'SALES DATA' table to a CSV file and use an external tool like Python's Matplotlib or Tableau to create the visualization.
• B. Create a Snowflake view that aggregates the daily sales data, then use Snowsight to visualize the view data as a table without any chart.
• C. Write a SQL query that uses 'DATE TRUNC('day', SALE DATE)' to group sales by day and calculate the total sales (SUM(QUANTITY PRICE)). Use Snowsight's line chart option with the truncated date on the x-axis and total sales on the y-axis, filtering by 'SALE_DATE' within the last year. Furthermore, use moving average with window function to smooth the data.
• D. Use the Snowsight web UI to manually filter the 'SALES_DATX table by 'SALE_DATE for the last year and create a bar chart showing 'SALE_ID count per day.
• E. Write a SQL query that calculates the daily total sales amount CSUM(QUANTITY PRICEY) for the last year and use Snowsight's charting options to generate a line chart with 'SALE DATE on the x-axis and daily sales amount on the y-axis.

You have trained a fraud detection model using scikit-learn and want to deploy it in Snowflake using the Snowflake Model Registry. You've registered the model as 'fraud _ model' in the registry. You need to create a Snowflake user-defined function (UDF) that loads and executes the model. Which of the following code snippets correctly creates the UDF, assuming the model is a serialized pickle file stored in a stage named 'model_stage'?

• A. Option D
• B. Option B
• C. Option C
• D. Option E
• E. Option A

Which of the following statements about Z-tests and T-tests are generally true? Select all that apply.

• A. A T-test is generally used when the sample size is large (n > 30) and the population standard deviation is known.
• B. As the sample size increases, the T-distribution approaches the standard normal (Z) distribution.
• C. Both Z-tests and T-tests assume that the data is non-normally distributed.
• D. A T-test has fewer degrees of freedom compared to the Z-test, making it more robust to outliers.
• E. A Z-test requires knowing the population standard deviation, while a T-test estimates it from the sample data.

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. 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. 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.