You have a Snowflake table named 'ORDERS' with columns 'ORDER D', 'CUSTOMER D', and 'ORDER JSON' (a variant column storing order details). You need to extract specific product names from the 'ORDER JSON' column for each order and return them as a table. The 'ORDER JSON' structure is an array of objects, where each object represents a product with fields like 'product_name' and 'quantity'. Which approach is the most efficient and scalable way to achieve this, considering the possibility of millions of rows in the 'ORDERS table?

• A. Use a standard SQL query with LATERAL FLATTEN and JSON VALUE functions to extract product names directly without using a UDF or UDTF.
• B. Create a Java UDF that takes ORDER JSON' as input, parses it using a JSON library, extracts the product names, and returns a comma-separated string. Use a WHILE loop within the UDF to parse the JSON array.
• C. Create a SQL UDF that iterates through the JSON array using SQL commands and returns a comma-separated string of product names. Then, use SPLIT TO_TABLE to convert the string to rows.
• D. Create a JavaScript UDF that takes ' ORDER_JSON' as input and returns an array of product names. Use 'JSON.parse()' to parse the JSON string and iterate using array methods.
• E. Create a Python UDTF that takes 'ORDER JSON' as input, parses it, and yields a row for each product name extracted. Use LATERAL FLATTEN within the UDTF for optimized JSON processing.

You're tasked with building an external function in Snowflake that calls an API to enrich customer data with geographical information (latitude and longitude) based on their IP address. The API endpoint requires an API key passed in the headen Your external function definition looks like this: "'sql CREATE OR REPLACE EXTERNAL FUNCTION VARCHAR) RETURNS VARIANT VOLATILE MAX BATCH ROWS = 100 RETURNS NULL ON NULL INPUT API INTEGRATION = AS 'https://api.example.com/geo'; Which of the following steps are essential to ensure the external function correctly passes the API key to the external service, handles rate limiting from the API, and correctly parses the JSON response from the external service (Assume the API returns a JSON object with 'latitude' and 'longitude' fields)?

• A. Create a Snowflake API integration object that includes the 'ALLOWED_HOSTS parameter pointing to 'https://api.example.com', and configure the service to return retry-after header on rate limit and ensure retryPolicy is set to 3 retries.
• B. Modify the external function code within the external service (e.g., AWS Lambda or Azure Function) to read the API key from an environment variable, construct the appropriate request headers (including the API key), implement exponential backoff with jitter for handling rate limits based on retry-after header if received, and ensure proper error handling for JSON parsing failures and return Snowflake compatible results.
• C. Implement robust logging within the external service (e.g., AWS Lambda or Azure Function) to capture details of requests, responses, and any errors encountered. Monitor these logs regularly to identify and address potential issues.
• D. Modify the 'enrich_ip' function to directly embed the API key within the API endpoint URL as a query parameter.
• E. Ensure the Snowflake user executing the function has the 'USAGE' privilege on the API integration object.

You are tasked with building a Snowpipe to ingest JSON data from an AWS S3 bucket into a Snowflake table named 'SALES DATA'. The data is landing in the bucket frequently, and you want to use Snowpipe's auto-ingest feature. However, you are observing significant latency in data appearing in your Snowflake table after it lands in S3, despite verifying that S3 event notifications are correctly configured and the Snowflake event queue is receiving them. You've already checked that the pipe is enabled and has the necessary permissions. The Snowflake Pipe definition is as follows:

What is the MOST LIKELY reason for this delay, and what steps can you take to further troubleshoot?

• A. Snowpipe auto-ingest only supports CSV files. Convert your JSON data to CSV format before loading.
• B. Snowflake's internal metadata cache is out of sync. Run 'ALTER PIPE SALES PIPE to refresh the cache.
• C. The S3 bucket is not in the same region as the Snowflake account. Ensure the S3 bucket and Snowflake account are in the same region to reduce network latency.
• D. The Snowflake virtual warehouse associated with the pipe is undersized. Increase the warehouse size to improve ingestion performance.
• E. There is a backlog of files in the internal Snowflake queue waiting to be processed. Monitor the 'SYSTEM$PIPE STATUS' function and consider increasing the 'MAX CONCURRENCY' parameter (if applicable, based on underlying infrastructure considerations) on the pipe definition.

You are tasked with implementing data masking on a 'CUSTOMER' table. The requirement is to mask the 'EMAIL' column for all users except those with the 'DATA ADMIN' role. You have the following code snippet. What is wrong with it?

• A. The WITH clause is unneccessary.
• B. Without masking poliy code, it's impossible to determine if there is anything wrong.
• C. There is no code provided, so there is nothing wrong with it.
• D. The masking policy syntax is incorrect. It should use 'CASE WHEN IS_ROLE_IN_SESSION('DATA_ADMIN') THEN EMAIL ELSE '[email protected]' END'.
• E. The masking policy is applied to the wrong column. It should be applied to the ID column, not the EMAIL column.

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' 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'.
• 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 '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'.
• D. 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
• 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'.