Tagging, classifying, and tracking lineage and quality across the data estate

Snowflake provides multiple system-level tools to monitor data usage, quality, and access patterns for governance purposes. This objective covers Account Usage views, Information Schema, and tagging mechanisms used to track and audit data across an account. Understanding these tools is essential for maintaining compliance and observability in a Snowflake environment.

1 · Learn the must-know

• 1ACCESS_HISTORY (in ACCOUNT_USAGE) records which columns and objects were actually read or written by queries, enabling data lineage and sensitive data access tracking.
• 2The DATA_QUALITY_MONITORING feature uses data metric functions (DMFs), both system-defined and user-defined, that can be scheduled to run automatically and log results to the DATA_METRICS view in ACCOUNT_USAGE.
• 3Object tagging combined with ACCOUNT_USAGE.TAG_REFERENCES lets governance teams identify and monitor where sensitive data (e.g., tagged as PII) exists across databases, schemas, tables, and columns.
• 4ACCOUNT_USAGE views have latency (typically up to 45 minutes to 3 hours) and longer retention (up to 1 year), whereas INFORMATION_SCHEMA table functions give near real-time data but only retain history for 7 days to 6 months depending on the view.
• 5The QUERY_HISTORY view/function can be filtered to monitor queries against specific tables or by specific roles/users, supporting auditing of who accessed what data and when.
• 6Automatic clustering, table storage metrics, and copy history can be monitored via ACCOUNT_USAGE views (e.g., AUTOMATIC_CLUSTERING_HISTORY, TABLE_STORAGE_METRICS, COPY_HISTORY) to track data volume, freshness, and load operations over time.

3 · Keep going

These are independent practice questions, written against this certification's published exam guide. They are not the certification vendor's own questions, and not the real exam.