ADA-C01 Question 192
Single answerManage data governance through SnowsightA Snowflake administrator is using Snowsight to help a governance team classify and protect sensitive customer data in a production database. The team wants a workflow that lets them discover potentially sensitive columns, review the findings in the UI, and then apply governance controls such as tags and masking policies to the affected columns. Which action should the administrator take in Snowsight to best support this requirement?
- A
Use Data > Databases to open the database, run a manual SQL query against INFORMATION_SCHEMA to guess which columns are sensitive, and then rely on object comments to enforce masking behavior.
- B
Use Snowsight's Governance or data discovery experience to review sensitive data classification results for columns, then apply tags and attach masking policies to tagged columns or directly to columns as needed.
- C
Use Resource Monitors in Snowsight to identify columns with high query activity, because frequently queried columns are automatically treated as sensitive and can then be masked from the same page.
- D
Use the Query History page in Snowsight to find users who accessed customer tables, because access history automatically classifies the underlying columns and creates masking policies for them.
Show answer and explanation
Correct answer: B
Explanation
The best answer is to use Snowsight's governance and sensitive data discovery/classification capabilities to review potentially sensitive columns and then apply governance controls such as tags and masking policies. This reflects how Snowflake supports data governance through Snowsight: administrators can work in a visual interface to inspect discovered classifications and manage policy-based protection. Best practice is to separate metadata discovery from enforcement: discovery/classification helps identify risk, while tags and masking policies implement protection. Comments, Query History, and Resource Monitors do not provide governance enforcement. This is consistent with Snowflake documentation on data governance features in Snowsight, including sensitive data discovery/classification, tags, and masking policies.
- A. Incorrect.
This is incorrect. Querying INFORMATION_SCHEMA can help inspect metadata, but it does not provide the intended Snowsight governance workflow for sensitive data discovery and review. More importantly, object comments do not enforce data protection. In Snowflake, governance enforcement is done with features such as tags, masking policies, row access policies, and related policy mechanisms, not comments.
- B. Correct.
This is correct. In Snowsight, administrators and governance teams can use the governance-oriented UI to review sensitive data classification/discovery results and then take action by applying tags and data protection policies. This aligns with a practical governance workflow: discover sensitive columns, review results visually, and enforce controls such as masking policies either directly on columns or via tag-based masking where appropriate.
- C. Incorrect.
This is incorrect. Resource Monitors are used to track and control credit consumption for warehouses and accounts; they are not data governance tools and do not classify sensitive columns. High query activity is not a signal that Snowflake automatically uses to designate a column as sensitive, and masking is not configured through Resource Monitors.
- D. Incorrect.
This is incorrect. Query History helps analyze executed SQL and troubleshoot workload behavior, but it does not automatically classify data or create masking policies. While access-related metadata can support governance investigations, classification and policy creation are separate governance tasks and are not automatically performed from Query History.