What considerations are crucial when performing table joins between parent/child tables for maintaining data integrity? (Select all that apply)

You are troubleshooting a slow-running query that performs a complex transformation on a large dataset using a series of CTEs. You've identified one particular CTE, , as contributing significantly to the overall execution time. Examination of the query profile reveals high spillover to local storage during the execution of this CTE. Which of the following actions could potentially reduce spillover and improve the performance of the CTE? Select all that apply.

You are designing a data pipeline to ingest JSON data from an external stage (AWS S3) into a Snowflake table called 'ORDERS' Some of the JSON files contain nested arrays that need to be flattened and transformed during the loading process. You have already defined a VARIANT column in the 'ORDERS table to store the raw JSON data'. However, occasionally, some files fail to load completely, and the 'SYSTEM$PIPE STATUS' shows a 'LOAD FAILED' status without providing granular details about the specific records causing the failure. Which of the following strategies, used IN COMBINATION, would be MOST effective in troubleshooting and resolving these failures while minimizing the impact on the overall data ingestion process?

You have a table 'CUSTOMER DATA with sensitive information like 'SSN' and 'CREDIT CARD NUMBER. You need to provide access to analysts for reporting purposes but must mask the sensitive columns. You decide to use a combination of Secure Views and data masking policies. Consider the following code snippets:

An analyst with the 'ANALYST ROLE executes the following query: SELECT FROM ANALYST CUSTOMER VIEW LIMIT 10; What will be the value of the 'SSN' column in the result set for the analyst?

When working with CSV, JSON, and Parquet data types in Snowflake, how does selecting the appropriate data format impact query performance and storage efficiency?