You're working with a large dataset containing customer purchase history. You want to identify customers whose purchase frequency deviates significantly from the average purchase frequency of all customers. The dataset is in a table named 'purchase history' with columns 'customer id' and 'purchase date'. What combination of Snowflake functionalities will allow you to achieve this task efficiently?
Choose all that apply.

• A. Calculate the Z-score of each customer's purchase frequency using 'AVG(Y, 'STDDEV()' , and window functions, and then filter based on a Z-score threshold.
• B. Calculate the average purchase frequency across all customers using and group by 'customer_id'.
• C. Use the window function to divide customers into quantiles based on their total purchase count.
• D. Employ the 'QUALIFY clause along with window functions to filter customers based on a condition related to their purchase frequency compared to the average.
• E. Create a UDF that computes the purchase frequency for a single user and apply it to all customers.

You are developing a model to predict customer churn using Snowflake ML. After training a Gradient Boosting model, you want to understand the relationship between 'number_of_products' and the churn probability. You generate a partial dependence plot (PDP) for 'number_of_products'. The PDP shows a steep increase in churn probability as 'number_of_products' increases from 1 to 3, followed by a plateau. Which of the following statements are the MOST accurate interpretations of this PDP? Assume the dataset is balanced and has undergone proper preprocessing.

• A. The model is perfectly calibrated, and the PDP accurately represents the true causal effect of 'number_of_products' on churn.
• B. Customers who purchase more than 3 products are less likely to churn, suggesting higher engagement or satisfaction.
• C. The PDP indicates a high degree of interaction between 'number_of_products' and other features in the model, making the interpretation unreliable.
• D. There might be a confounding variable correlated with both 'number_of_products' and churn, leading to a spurious relationship in the PDP.
• E. Increasing the number of products purchased by all customers will definitively reduce overall churn.

You have built an external function to train a PyTorch model using SageMaker. The model training process requires a significant amount of CPU and memory. The training data is passed from Snowflake to the external function in batches. The external function code in AWS Lambda is as follows:

The Snowflake external function is defined as follows:

During testing, you encounter '500 Internal Server Error' from the external function consistently. Upon inspection of the Lambda logs, you find messages indicating 'PayloadTooLargeError'. What is the most likely cause and how do you mitigate it within the context of Snowflake and AWS Lambda?

• A. The IAM role associated with the Lambda function lacks the necessary permissions to invoke the SageMaker training job. Grant the Lambda function's IAM role the appropriate SageMaker permissions.
• B. The Snowflake external function definition is incorrect. Change the 'RETURNS VARIANT clause to 'RETURNS VARCHAR as the Lambda function returns a JSON string.
• C. The size of the data being sent from Snowflake to the Lambda function exceeds the maximum payload size allowed by AWSAPI Gateway. Increase the maximum payload size limit in the API Gateway settings.
• D. The size of the data being sent from Snowflake to the Lambda function exceeds the maximum payload size allowed by AWS API Gateway. Implement data partitioning in Snowflake and send smaller batches of data to the Lambda function, aggregating the results in a separate table.
• E. The Lambda function is timing out before the model training can complete. Increase the Lambda function's timeout setting to allow sufficient time for the training process.

You've trained a sales forecasting model using Snowpark ML and want to deploy it within Snowflake for real-time predictions. You've decided to store the predictions directly in a Snowflake table. The model predicts sales for different product categories based on historical data and promotional activities. Which of the following approaches is the MOST efficient and scalable way to store these predictions, considering a high volume of prediction requests and the need for quick retrieval for downstream dashboards?

• A. Storing predictions in a single, wide table with all features and predictions as columns. No partitioning or clustering is implemented.
• B. Storing predictions in a VARIANT column in a single table. All prediction results for a given product category are stored as a JSON document within the VARIANT column.
• C. Storing predictions in a key-value store like Redis and referencing the keys from a Snowflake table. Requires external network access from Snowflake.
• D. Storing predictions in a separate table with a composite key of product category and timestamp, with clustering on the timestamp column and partitioning by product category.
• E. Storing predictions in an external stage (e.g., AWS S3) and querying them using an external table. The external table definition includes the sales prediction as a column.

You're deploying a pre-trained model for fraud detection that's hosted as a serverless function on Google Cloud Functions. This function requires two Snowflake tables: 'TRANSACTIONS (containing transaction details) and 'CUSTOMER PROFILES (containing customer information), to be joined and used as input for the model. The external function in Snowflake, 'DETECT FRAUD', should process batches of records efficiently. Which of the following approaches are most suitable for optimizing data transfer and processing between Snowflake and the Google Cloud Function?

• A. Create a Snowflake pipe that automatically streams new transaction data to the Google Cloud Function whenever new records are inserted into the 'TRANSACTIONS' table, triggering the fraud detection model in real-time.
• B. Utilize Snowflake's external functions feature to send batches of data from the joined 'TRANSACTIONS' and 'CUSTOMER PROFILES tables to the 'DETECT_FRAUD function in a structured format (e.g., JSON) using HTTP requests. Implement proper error handling and retry mechanisms.
• C. Use Snowflake's Java UDF functionality to directly connect to the Google Cloud Function's database, bypassing the need for an external function or data transfer through HTTP.
• D. Serialize the joined 'TRANSACTIONS' and 'CUSTOMER_PROFILES data into a large CSV file, store it in a cloud storage bucket, and then pass the URL of the CSV file to the 'DETECT FRAUD function.
• E. Within the 'DETECT FRAUD function, execute SQL queries directly against Snowflake using the Snowflake JDBC driver to fetch the necessary data from the "TRANSACTIONS' and 'CUSTOMER PROFILES' tables.