Row Access Policies (RAP) in Snowflake provide the most secure and efficient way to implement row-level security. By defining policies based on the user's role, you can ensure that users only see the data they are authorized to access directly at the Snowflake level. Combining RAPs with dynamic data masking for 'SALE_AMOUNT adds an extra layer of security by redacting sensitive data based on the user's role. Option A requires managing multiple views, which can be cumbersome. Option B introduces complexity with stored procedures. Option D moves security logic into Looker Studio, which is less secure and can be bypassed. Using a service account with appropriate privileges ensures that Looker Studio can access the data securely and apply the defined Row Access Policies. Options C is correct because it keeps security logic within Snowflake. Option E is correct because it provides additional data masking depending on the security policies for sale amount.

Creating an external table and explicitly specifying the file format with 'COMPRESSION = 'SNAPPY" allows Snowflake to efficiently access the data. 'AUTO REFRESH = TRUE' ensures schema evolution is handled, and creating the external table directly allows you to leverage Parquet's columnar storage. 'ANALYZE TABLE allows Snowflake to optimize the query plan. While Snowflake can automatically detect compression in some cases, explicitly specifying it provides more control and can improve performance. 'OPTIMIZE TABLE' is not applicable to external tables. Materialized views on top of External tables is computationally expensive. Using variant column is not optimized approach as it bypasses the benefits of Parquet files.

Native data types in Snowflake offer flexibility in handling varied structures of semi-structured data, allowing for a more adaptable approach compared to traditional relational data types.

Materialized views' effectiveness depends on factors like data update frequency and query result caching, impacting query optimization based on the nature of data updates and caching capabilities.

Options A and C are both correct. Option A uses 'REGEXP_LIKE with two separate regular expressions to search for a 16-digit number (credit card) and a 'XXX-XX-XXXX' pattern (SSN). Option C uses a single 'REGEXP LIKE' function with an alternation to combine both patterns into one regular expression , which is generally more efficient. Option B relies on 'CONTAINS' which performs a simple substring search, not regular expression matching; this will not accurately identify the patterns. Option D uses 'STARTS WITH' which will only identify descriptions that begin with the specified keywords, missing most cases. Option E uses 'LIKE operator for the mentioned words in the sentence that can be 'Credit Card' or 'Social Security Number', but this isn't a search for Credit card numbers or SSNs.