You are tasked with migrating data from a legacy SQL Server database to Snowflake. One of the tables, 'ORDERS' , contains a column 'ORDER DETAILS that holds concatenated string data representing multiple order items. The data is formatted as 'iteml :qtyl ;item2:qty2;...'. You need to transform this string data into a JSON array of objects, where each object represents an item with 'name' and 'quantity' fields. Which of the following steps and functions would you use in Snowflake to achieve this transformation, in addition to loading the data?

• A. Use ' STRTOK TO ARRAY' to split the string into an array, then iterate through the array using a JavaScript UDF to create the JSON objects.
• B. Utilize a Java UDF to parse the string and directly generate the JSON array.
• C. Use ' to extract item names and quantities, then use 'ARRAY_CONSTRUCT and 'OBJECT_CONSTRUCT to create the JSON array.
• D. Use 'SPLIT with ';' as delimiter, then apply 'SPLIT again with ':' as delimiter. Finally, construct the JSON array using 'ARRAY_AGG' and 'OBJECT CONSTRUCT
• E. Use to split the string into rows, then use 'SPLIT to separate item name and quantity, and finally use 'OBJECT_CONSTRUCT and to create the JSON array.

A Snowflake table 'ORDERS' is clustered on the 'ORDER DATE column. After several months, you notice that many micro-partitions contain data from a wide range of 'ORDER DATE values, and query performance on date range filters is degrading. Which of the following actions could improve performance and reduce the overlap in micro-partitions?

• A. Change the clustering key to a composite key including ORDER DATE and another relevant column.
• B. Drop and recreate the table with a different clustering key.
• C. Increase the virtual warehouse size used for loading data into the "ORDERS' table.
• D. Run 'OPTIMIZE TABLE ORDERS' to recluster the table.
• E. Change the ORDER DATE column to VARCHAR and then cluster.

You're using Snowpark Python to transform data in a Snowflake table called 'employee_data' which includes columns , 'department, 'salary' , and 'performance_rating'. You need to identify the top 3 highest-paid employees within each department based on their salary, but only for departments where the average performance rating is above 4.0. Which of the following approaches using Snowpark efficiently combines window functions, filtering, and aggregations to achieve this?

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

You've created a JavaScript UDF in Snowflake to perform complex string manipulation. You need to ensure this UDF can handle a large volume of data efficiently. The UDF is defined as follows:

When testing with a large dataset, you observe poor performance. Which of the following strategies, when applied independently or in combination, would MOST likely improve the performance of this UDF?

• A. Convert the JavaScript UDF to a Java UDF, utilizing Java's more efficient string manipulation libraries and leveraging Snowflake's Java UDF execution environment.
• B. Ensure the input 'STRING' is defined with the maximum possible length to provide sufficient memory allocation for the JavaScript engine to manipulate the string.
• C. Increase the warehouse size to the largest available size (e.g., X-Large) to provide more resources for the UDF execution.
• D. Pre-compile the regular expressions used within the JavaScript UDF outside of the function and pass them as constants into the function. JavaScript regex compilation is expensive, and pre-compilation can reduce overhead.
• E. Replace the JavaScript UDF with a SQL UDF that uses built-in Snowflake string functions like 'REGEXP REPLACE and 'REPLACE. SQL UDFs are generally more optimized within Snowflake's execution engine.

A data engineering team is building a real-time dashboard in Snowflake to monitor website traffic. The dashboard relies on a complex query that joins several large tables. The query execution time is consistently exceeding the acceptable threshold, impacting dashboard responsiveness. Historical data is stored in a separate table and rarely changes. You suspect caching is not being utilized effectively. Which of the following actions would BEST improve the performance of this dashboard and leverage Snowflake's caching features?

• A. Replace the complex query with a series of simpler queries. This will reduce the amount of data that needs to be processed at any one time.
• B. Use 'RESULT_SCAN' to cache the query result in the user session for subsequent queries. This is especially effective for large datasets that don't change frequently.
• C. Create a materialized view that pre-computes the results of the complex query. Snowflake will automatically refresh the materialized view when the underlying data changes.
• D. Materialize the historical data into a separate table that utilizes clustering and indexing for faster query performance. Refresh this table periodically.
• E. Increase the size of the virtual warehouse. A larger warehouse will have more resources to execute the query, and the results will be cached for a longer period.