A data practitioner is configuring a data transformation pipeline in Google Cloud using Dataform. To ensure compliance with business rules and quality standards before downstream analytics processing, the dataset must meet specific validation requirements: The email column must match a specific format pattern (%@%.%). The signup_date column must fall within a valid date range (greater than 2022-08-01). The quality checks must run automatically during workflow updates, failing if any row violates these constraints. Which feature in Dataform should the practitioner configure in the table definition?

Configure custom rowConditions assertions within the config block of the Dataform table: ### What Dataform rowConditions Assertions Are In Google Cloud Dataform, **assertions** are automated data quality testing mechanisms executed during workflow runs. Within a table definition's `config` block, **`rowConditions`** allows data practitioners to define custom SQL expressions that every row in the resulting table must satisfy. If any row evaluates to `false` against any defined row condition, Dataform flags the assertion as failed. ### How it Meets the Business Requirements - **Pattern Matching (✓):** A custom SQL expression such as `'email like "%@%.%"'` can be added directly to the `rowConditions` array to verify that string values conform to expected email patterns. - **Range Validation (✓):** Expressions like `'signup_date is null or signup_date > "2022-08-01"'` ensure that date values stay within permissible historical boundaries. - **Automated Workflow Quality Gate (✓):** Dataform evaluates these row conditions every time it updates the workflow and records failing rows in an automatically created assertion view in BigQuery (under the `dataform_assertions` dataset). ### Key Capabilities and Advantages - **Declarative Quality Control:** Quality tests are declared directly inside the table's SQLX configuration block alongside transformation logic. - **Automatic Failure Detection:** Any record that violates the boolean logic causes the assertion to fail, preventing silent data corruption. - **Inspection Views:** Dataform automatically publishes compiled assertion queries as BigQuery views so practitioners can inspect invalid rows. - **Comprehensive Rule Support:** Supports any valid SQL boolean predicate, including range checks, regex comparisons, arithmetic boundaries, and conditional logic. ### Why This Is Optimal Using `rowConditions` inside the table's `config` block provides a built-in, native method to enforce pattern and range constraints without needing to write external validation pipelines or separate testing scripts.