You are working with a Snowflake table 'customer_data' which contains customer information stored in a VARIANT column named raw_info'. The 'raw_info' JSON structure includes nested addresses, and preferences. Your task is to extract the city from the first address in the 'addresses' array, and the customer's preferred communication method from the 'preferences' object. Some customers might not have addresses or preferences defined. Select the two SQL snippets that correctly and efficiently extract this data, handling missing fields gracefully and providing appropriate type casting. Address array is in the format 'addresses: [ { 'city': '...', 'state': ' '},

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

You are designing a data protection strategy for a Snowflake database. You need to implement dynamic data masking on the 'CREDIT CARD' column in the 'TRANSACTIONS' table. The requirement is that users with the 'FINANCE ADMIN' role should see the full credit card number, while all other users should see only the last four digits. You have the following masking policy:

What is the next step to apply this masking policy to the 'CREDIT CARD' column?

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

Consider the following Snowflake Javascript UDF designed to convert temperatures between Celsius and Fahrenheit:

Which statement regarding the UDF's security and behavior is MOST accurate?

• A. The 'SECURE' keyword ensures that the UDF's code is completely hidden from all users, including those with the ACCOUNTADMIN role.
• B. The UDF is vulnerable to SQL injection because the 'unit' parameter is not validated against a predefined list of acceptable values.
• C. While the ' SECURE keyword hides the UDF's source code from users without sufficient privileges, users with the ACCOUNTADMIN role can still view the source code.
• D. The UDF will always return a value, even if the input 'unit is invalid, due to the implicit type conversion in JavaScript.
• E. The UDF is not vulnerable to SQL injection because Javascript UDFs in Snowflake are sandboxed and prevent direct SQL execution.

You are building a data pipeline that involves ingesting data from AWS S3 into Snowflake using Snowpipe. The data arrives in small files frequently, and you are experiencing performance issues with delayed data availability. You need to optimize the pipeline for near real-time ingestion. Which combination of strategies will MOST effectively address this scenario?

• A. Implement a micro-batching process using a third-party tool (like Apache Spark) to aggregate the small files into larger batches before loading them into S3, then configure Snowpipe to ingest the larger files.
• B. Enable auto-ingest on the Snowpipe and increase the frequency of S3 event notifications to Snowflake. Combine this with clustering the target table on a relevant column to optimize query performance after loading.
• C. Manually refresh the Snowpipe using the 'ALTER PIPE ... REFRESH' command every few minutes to force ingestion of new data.
• D. Increase the warehouse size used for the Snowpipe load process and adjust the 'MAX FILE SIZE' parameter on the pipe definition to match the size of the incoming files.
• E. Configure S3 event notifications to trigger Snowpipe only when a sufficient number of files have arrived in the S3 bucket, using a serverless function (like AWS Lambda) to manage the file count threshold.

You have a table 'EMPLOYEE DATA' containing Personally Identifiable Information (PII), including 'salary' and 'email'. You need to implement column-level security such that: 1) The 'salary' column is only visible to users in the 'FINANCE ROLE. 2) The 'email' column is masked with a SHA256 hash for all users except those in the 'HR ROLE. You create the following masking policies:

Which of the following SQL statements correctly applies these masking policies to the 'EMPLOYEE DATA table?

• A. ALTER TABLE EMPLOYEE_DATAALTER COLUMN salary SET MASKING POLICY mask_salary; ALTER TABLE EMPLOYEE_DATAALTER COLUMN email SET MASKING POLICY mask email;
• B. ALTER TABLE EMPLOYEE_DATAAPPLY MASKING POLICY mask_salary ON COLUMN salary; ALTER TABLE EMPLOYEE_DATAAPPLY MASKING POLICY mask email ON COLUMN email;
• C. CREATE OR REPLACE TAG employee_data.salary VALUE 'mask_salary'; CREATE OR REPLACE TAG employee_data.email VALUE 'mask_email';
• D. ALTER TABLE EMPLOYEE_DATA MODIFY COLUMN salary SET MASKING POLICY = mask_salary; ALTER TABLE EMPLOYEE_DATA MODIFY COLUMN email SET MASKING POLICY = mask email;
• E. ALTER TABLE EMPLOYEE_DATA MODIFY COLUMN salary SET MASKING POLICY mask_salary; ALTER TABLE EMPLOYEE_DATA MODIFY COLUMN email SET MASKING POLICY mask email;