You are tasked with implementing a projection policy in Snowflake to restrict access to certain columns of the 'EMPLOYEE table based on the user's role. The table contains columns like 'EMPLOYEE 'NAME, 'SALARY', and 'DEPARTMENT. Users with the 'HR MANAGER role should have access to all columns, while other users should only be able to see 'EMPLOYEE ID, 'NAME, and DEPARTMENT. The initial attempt to create the projection policy results in an error. What could be the reasons?

• A. Projection policies are not supported in Snowflake.
• B. The user attempting to create the projection policy does not have the 'OWNERSHIP' privilege on the 'EMPLOYEE table.
• C. Projection policies can only be applied at the database level, not at the table level.
• D. The EMPLOYEE table must have row-level security policies enabled before applying a projection policy.
• E. The projection policy definition might contain syntax errors or reference non-existent roles or columns.

You are developing a data pipeline in Snowflake that uses SQL UDFs for data transformation. You need to define a UDF that calculates the Haversine distance between two geographical points (latitude and longitude). Performance is critical. Which of the following approaches would result in the most efficient UDF implementation, considering Snowflake's execution model?

• A. Create an External Function (using AWS Lambda or Azure Functions) to calculate the Haversine distance. This allows for offloading the computation to a separate compute environment.
• B. Create a SQL UDF that pre-calculates the RADIANS for latitude and longitude only once and stores them in a temporary table, using those values for subsequent distance calculations within the same session.
• C. Create a SQL UDF leveraging Snowflake's VECTORIZED keyword, hoping to automatically leverage SIMD instructions, without any code changes to mathematical calculation inside the UDF
• D. Create a Java UDF that calculates the Haversine distance, leveraging optimized mathematical libraries. This allows for potentially faster execution due to lower- level optimizations.
• E. Create a SQL UDF that directly calculates the Haversine distance using Snowflake's built-in mathematical functions (SIN, COS, ACOS, RADIANS). This is straightforward and easy to implement.

You are working with a very large Snowflake table named 'CUSTOMER TRANSACTIONS which is clustered on 'CUSTOMER ID and 'TRANSACTION DATE. After noticing performance degradation on queries that filter by 'TRANSACTION AMOUNT and 'REGION' , you decide to explore alternative clustering strategies. Which of the following actions, when performed individually, will LEAST likely improve query performance specifically for queries filtering by 'TRANSACTION AMOUNT and 'REGION', assuming you can only have one clustering key?

• A. Creating a materialized view that pre-aggregates data by 'TRANSACTION_AMOUNT and 'REGION'.
• B. Creating a search optimization on 'TRANSACTION_AMOUNT' and 'REGION' columns.
• C. Creating a new table clustered on 'TRANSACTION_AMOUNT and 'REGION', and migrating the data.
• D. Dropping the existing clustering key and clustering on 'TRANSACTION_AMOUNT' and 'REGION'.
• E. Adding ' TRANSACTION_AMOUNT and 'REGIO!V to the existing clustering key while retaining 'CUSTOMER_ID and 'TRANSACTION_DATE

A global e-commerce company, 'GlobalMart', uses Snowflake for its data warehousing needs. They operate primarily in the US (us-east-1) and Europe (eu-west-l). They're implementing cross-region replication for disaster recovery and business continuity. Their requirements are: 1) All data from the US region needs to be replicated to the EU region. 2) The failover to the EU region should have minimal downtime. 3) Replication should be automatic and continuous. Considering these requirements, which of the following Snowflake features and configurations would be the MOST suitable and efficient?

• A. Manually unload data from the US region and load it into the EU region using SnowSQL. Automate this process using a scheduled task.
• B. Export data from the US region to cloud storage (e.g., AWS S3 or Azure Blob Storage) and then load it into the EU region using Snowpipe.
• C. Use Snowflake's Data Sharing feature to share data from the US region with an account in the EU region. This automatically replicates the data.
• D. Create a database replica in the EU region and manually refresh it periodically using 'CREATE DATABASE AS CLONE'
• E. Enable database replication using replication groups, configure a primary database in us-east-I , and a secondary database in eu-west-l. Set the replication schedule with 'ALTER REPLICATION GROUP ADD .

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 ' to extract item names and quantities, then use 'ARRAY_CONSTRUCT and 'OBJECT_CONSTRUCT to create the JSON array.
• B. Utilize a Java UDF to parse the string and directly generate the JSON array.
• C. Use 'SPLIT with ';' as delimiter, then apply 'SPLIT again with ':' as delimiter. Finally, construct the JSON array using 'ARRAY_AGG' and 'OBJECT CONSTRUCT
• D. 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.
• 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.