You have a VARIANT column named 'raw_data' in a Snowflake table 'eventS , containing nested JSON data'. You need to extract specific fields Cevent_id', 'timestamp' , and 'user.user_id') and load them into a relational table 'structured_events' with columns 'event_id' , 'timestamp' , and 'user_id', respectively. However, some entries may be missing the 'user' object. Which of the following SQL statements will achieve this while handling missing 'user' objects gracefully and ensuring data integrity, and also efficiently handle potentially large JSON payloads?

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

You are tasked with creating a resilient data ingestion pipeline using Snowpipe and external tables on AWS S3. The data consists of JSON files, some of which may occasionally contain invalid JSON structures (e.g., missing closing brackets, incorrect data types). You want to ensure that even if some files are corrupted, the valid data is still ingested into your target Snowflake table, and the corrupted files are logged for later investigation. Which of the following steps would BEST achieve this?

• A. Create a custom error handler using a Snowflake stored procedure that catches the 'JSON PARSER ERROR exception and logs the filename to a separate error table. Use the ERROR = 'CONTINUE" copy option in the Snowpipe definition.
• B. Configure Snowpipe to use the 'ON ERROR = 'SKIP FILE" copy option and then create a separate task to query the 'VALIDATION MODE metadata column in the external table to identify and log the corrupted files.
• C. Use Snowflake's => 'JSON', job_id => function against the external stage before ingesting data with Snowpipe to pre-validate files. Then ingest only validated files to your target table
• D. Set the 'ON ERROR option to 'ABORT STATEMENT in the Snowpipe definition. This will stop the entire Snowpipe process when a JSON error is detected, allowing you to manually investigate and fix the corrupted files before restarting the pipeline.
• E. Configure the external table definition with 'VALIDATION MODE = 'RETURN ERRORS" and then create a view on top of the external table that filters out rows where the 'METADATA$FILE ROW NUMBER column contains errors.

You have configured a replication group to replicate a database 'CUSTOMER DATA' from your primary Snowflake account (AWS us- east-I) to your secondary Snowflake account (Azure eastus). After a recent network outage, the replication process stopped. Upon investigation, you find that some tables in the 'CUSTOMER DATA' database in the primary account have been modified (schema changes). You need to resume replication and ensure data consistency in the secondary account, with minimal impact on users querying the secondary database. Which of the following commands or sequence of commands would be the MOST appropriate to refresh the secondary database while minimizing downtime for query users?

• A. First, suspend the replication group with 'ALTER REPLICATION GROUP SUSPEND;'. Then, drop the secondary database 'DROP DATABASE . Finally, recreate the secondary database as a replica: 'CREATE DATABASE AS REPLICA OF and resume the replication group 'ALTER REPLICATION GROUP RESUME;'.
• B. Issue a 'ALTER DATABASE REFRESH FROM command. This command automatically handles schema changes and ensures data consistency with minimal downtime.
• C. Suspend the replication group with 'ALTER REPLICATION GROUP SUSPEND;' Then execute the following: 'ALTER DATABASE ENABLE REPLICATION TO ACCOUNT ALTER REPLICATION GROUP RESUME;'
• D. Issue a 'ALTER REPLICATION GROUP REFRESH$ command. This will automatically handle schema changes and resume replication.
• E. First, pause all running queries on the secondary account. Then, execute a full refresh using 'ALTER DATABASE REFRESH FROM Finally, resume the queries on the secondary account.

Which of the following statements are true regarding data masking policies in Snowflake? (Select all that apply)

• A. Data masking policies can be applied to both tables and views.
• B. The 'CURRENT_ROLE()' function can be used within a masking policy to implement role-based data masking.
• C. Data masking policies are supported on external tables.
• D. Once a masking policy is applied to a column, the original data is permanently altered.
• E. Different masking policies cannot be applied to different columns within the same table.

You are tasked with building a User-Defined Aggregate Function (UDAF) in Snowflake to calculate the weighted average of product prices. The weight is determined by the quantity sold for each product. You have the following table: 'SALES (product_id INTEGER, price quantity INTEGER)'. Which of the following UDAF definitions would correctly calculate the weighted average?

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