DEA-C02 Exam Dumps | A data engineer is tasked with implementing a data governance strategy in Snowflake. They need to automatically

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Question 123/149

A data engineer is tasked with implementing a data governance strategy in Snowflake. They need to automatically apply a tag 'PII CLASSIFICATION' to all columns containing Personally Identifiable Information (PII). Given the following requirements: 1. The tag must be applied as close to data ingestion as possible. 2. The tagging process should be automated and scalable. 3. The tag value should be dynamically set based on a regular expression match against column names and data types. Which of the following approaches would be MOST effective and efficient in achieving these goals?

A. Create a Snowflake Task that runs daily, querying the INFORMATION SCHEMCOLUMNS view, identifying potential PII columns based on regular expressions, and then executing ALTER TABLE ... ALTER COLUMN ... SET TAG commands.

B. Implement a custom application using the Snowflake JDBC driver to periodically scan table schemas, detect PII columns, and apply tags using dynamic SQL.

C. Use Snowflake's Event Tables in conjunction with a stream and task. Configure the stream to capture DDL changes, and the task to evaluate new columns and apply the tag based on the column metadata using regular expressions.

D. Manually tag each column containing PII using the Snowflake web UI or the 'ALTER TABLE ... ALTER COLUMN ... SET TAG' command. Train data stewards to identify and tag new columns.

E. Implement a stored procedure that leverages external functions to call a Python script hosted on AWS Lambda, which uses a machine learning model to identify PII and apply Snowflake tags.

Correct Answer: C

Option C is the most effective because it leverages Snowflake's native event capture mechanisms (Event Tables, Streams and Tasks) to react to DDL changes in near real-time. This approach is automated, scalable, and avoids the overhead of periodic polling. Options A and B involve periodic scanning which is less efficient. Option D is manual and doesn't scale. Option E introduces unnecessary complexity with external functions and ML models for a relatively simple task, increasing operational overhead.

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