Consider a scenario where you need to transform data in a Snowflake table using a complex custom transformation logic best implemented in Java'. You decide to use a Snowpark Java UDF. You've packaged your Java code into a JAR file and uploaded it to an internal stage named Which of the following steps are necessary and correctly ordered to deploy and use this Java UDF within Snowflake?

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

You are designing a data product for the Snowflake Marketplace that provides daily weather forecasts. You need to ensure that consumers of your data receive the latest forecast data every morning automatically with minimal latency. Which of the following strategies offers the MOST efficient and cost-effective solution for updating the shared data?

• A. Create a stored procedure that truncates and reloads the shared tables with the latest forecast data from a staging table. Schedule this stored procedure to run every morning at 6 AM using a Snowflake task.
• B. Implement a continuous data pipeline using Snowflake Streams and Tasks to incrementally update the shared tables as new forecast data becomes available. The stream tracks changes and tasks apply those changes to the shared tables.
• C. Share the raw data files stored in an external stage with the consumers. Consumers will then need to create their own pipelines to process and load the data.
• D. Create a scheduled task that executes a full refresh of the shared tables every morning at 6 AM. This task uses CREATE OR REPLACE TABLE AS SELECT to rebuild the tables with the latest forecast data.
• E. Manually upload a new CSV file containing the latest forecast data to a Snowflake stage and then load it into the shared tables every morning at 6 AM.

You are tasked with implementing column-level security on the 'EMPLOYEE table to restrict access to the 'SALARY column. Only users with the 'HR ROLE' should be able to view the actual salary. All other users should see NULL. You create a masking policy as follows:

What additional steps are necessary to enforce this policy?

• A. Apply the masking policy to the EMPLOYEE table: ALTER TABLE EMPLOYEE SET MASKING POLICY salary_mask;
• B. Apply the masking policy to the SALARY column: ALTER TABLE EMPLOYEE MODIFY COLUMN SALARY SET MASKING POLICY salary_mask;
• C. Grant the APPLY MASKING POLICY privilege to the HR_ROLE.
• D. Grant OWNERSHIP on the masking policy to the HR ROL
• E. Grant the SELECT privilege on the EMPLOYEE table to the HR ROLE.

You have a table 'EVENTS' containing application event data with columns 'EVENT ID, 'USER ID, 'EVENT TYPE, and EVENT DETAILS (VARCHAR). The 'EVENT DETAILS column contains comma-separated key-value pairs (e.g., 'location=USA,device=mobile,os=iOS'). Your objective is to transform this structured data into a VARIANT column named EVENT JSON' in a new table 'EVENTS JSON'. The data in EVENT DETAILS has inconsistent key-value pairs across different rows. Which of the following methods are the most efficient and scalable to parse the key-value pairs in 'EVENT DETAILS' and construct the JSON objects?

• A. Use a combination of 'SPLIT, 'REGEXP_REPLACE and 'OBJECT_CONSTRUCT within a user-defined function (UDF) to parse the string and build the JSON object.
• B. Use only REGEXP EXTRACT ALL' with appropriate regular expressions to extract all keys and values into arrays, then use a JavaScript UDF to combine them into a JSON object.
• C. Use a Java UDF that iterates through the string, splitting it based on commas and equals signs, and then constructs a JSON object using a JSON library.
• D. Utilize to split the key-value pairs into rows, then use 'REGEXP_EXTRACT to extract the key and value. Finally, use 'OBJECT_CONSTRUCT and to construct the JSON object.
• E. Use 'SPLIT to split the key-value pairs into an array, then use a LATERAL FLATTEN to create rows from array, then use 'SPLIT again to split each row by '='. Finally, construct the JSON using 'OBJECT CONSTRUCT.

You are ingesting data from an AWS S3 bucket into a Snowflake table using a COPY INTO statement. The COPY INTO command fails with an error indicating 'Invalid stage location specified'. You have verified that the stage name is correct and the Snowflake user has the necessary privileges to access the stage. However, the error persists. Which of the following are potential causes and solutions for this issue?

• A. The external stage definition in Snowflake includes an incorrect storage integration. Examine and correct the STORAGE INTEGRATION parameter in the CREATE STAGE statement.
• B. The S3 bucket is encrypted using KMS and the Snowflake integration lacks the necessary key grant. Check the KMS key policy to ensure the storage integration IAM role has decrypt permission.
• C. The IAM role associated with the Snowflake stage is incorrect or does not have sufficient permissions to access the S3 bucket. Verify the IAM role configuration and permissions.
• D. The S3 bucket policy is not correctly configured to allow Snowflake to assume the IAM role. Review the bucket policy to ensure it grants access to the Snowflake IAM role.
• E. The network policy configured in Snowflake is blocking access to the AWS S3 endpoint. Check the network policy rules and ensure they allow outbound traffic to the S3 region.