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

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

You need to implement a data masking solution in Snowflake for a table 'CUSTOMER DATA' containing PII. The requirement is to mask the email address based on the user's role: if the user is in 'ANALYST ROLE , the email address should be partially masked (e.g., 'a @example.com'), otherwise, it should be fully masked (e.g., @ .com'). Which of the following masking policy definitions and subsequent actions will correctly implement this?

• A. Create a masking policy 'email_mask' that always fully masks the email address. Grant the 'UNMASK' privilege on the 'EMAIL' column to the 'ANALYST ROLE
• B. Create two separate masking policies, one for 'ANALYST_ROLE' and one for all other roles. Apply both policies to the 'EMAIL' column of 'CUSTOMER DATA'. Grant the 'APPLY MASKING POLICY privilege on the 'CUSTOMER DATA' table to the 'ANALYST_ROLE.
• C. Create a masking policy 'email_mask' using a 'CASE' statement that checks 'CURRENT_ROLE()'. If the role is 'ANALYST_ROLE, partially mask using 'LEFT and 'REGEXP REPLACE; otherwise, fully mask using 'REGEXP REPLACE. Apply this policy to the 'EMAIL' column of 'CUSTOMER DATA'.
• D. Create a masking policy 'email_mask' using 'REGEXP_REPLACE to replace the first part of the email with asterisks if the current role is not 'ANALYST_ROLE' , otherwise use 'LEFT and ' REGEXP_REPLACE to mask only part of the username. Apply this policy to the 'EMAIL ' column of 'CUSTOMER DATA'.
• E. Create a masking policy 'email_mask' using a 'CASE' statement that checks 'CURRENT_ROLE()'. If the role is 'ANALYST_ROLE, partially mask using 'LEFT and 'REGEXP REPLACE; otherwise, return original value. Apply this policy to the 'EMAIL' column of 'CUSTOMER DATA'.

You're designing a near real-time data pipeline for clickstream data using Snowpipe Streaming. The data volume is extremely high, with bursts exceeding 1 million events per second. Your team reports intermittent ingestion failures and latency spikes. Considering the constraints of Snowpipe Streaming, which of the following strategies would be MOST effective in mitigating these issues, assuming the data format is optimized and network latency is minimal?

• A. Switch from Snowpipe Streaming to Classic Snowpipe, as it is more resilient to high data volumes.
• B. Implement a message queue (e.g., Kafka) in front of Snowpipe Streaming to buffer incoming events and smooth out the traffic spikes.
• C. Reduce the size of each micro-batch being sent to Snowpipe Streaming to minimize the impact of individual failures.
• D. Implement client-side retry logic with exponential backoff and jitter to handle transient errors and avoid overwhelming the service.
• E. Increase the number of Snowflake virtual warehouses to handle the increased load.