Implement and Manage Data Quality Constraints in Unity Catalog DP-750 Practice Questions (Page 4)

Questions 16–20

1. 16foundation · easy

   Which Delta Lake feature allows a table's schema to automatically adapt to new columns in incoming data?

   • ATime travel
   • BSchema evolution
   • CSchema enforcement
   • DChange data feed
   Reveal Solution Discussion Select an answer first
2. 17application · medium

   An e-commerce company has a `product_catalog` table that is updated by multiple teams. One team recently added a `color` column to their data source, and they want to include it in the table without losing any existing data or breaking downstream consumers. The downstream consumers are not yet ready to handle the new column. What should the team do?

   • AUse `MERGE` with a condition that only updates existing columns and ignores the new column.
   • BUse `ALTER TABLE ... ADD COLUMNS` to add the `color` column, and let downstream consumers continue to query the existing columns.
   • CSet the table property `delta.columnMapping.mode` to 'none' to prevent schema changes.
   • DDrop and recreate the table with the new schema, then reload all data from the source.
   Reveal Solution Discussion Select an answer first
4. 18foundation · easy

   In Lakeflow Spark Declarative Pipelines, what is the purpose of pipeline expectations?

   • ATo define the storage location for pipeline output
   • BTo define the compute resources required for the pipeline
   • CTo define data quality constraints that must be satisfied by data in the pipeline
   • DTo define the schedule on which the pipeline runs
   Reveal Solution Discussion Select an answer first
5. 19application · medium

   A healthcare analytics company ingests patient vitals data from IoT devices into a Unity Catalog managed table. The ingestion job occasionally produces rows where the `heart_rate` column is NULL or contains implausible values like 0 or 500. The team needs to ensure that only rows with a heart rate between 30 and 220 are written to the table, and any invalid rows should be quarantined for review. What should the data engineer configure?

   • AApply a data type check to ensure `heart_rate` is an integer, and cast any non-integer values to NULL.
   • BAdd a NOT NULL constraint on the `heart_rate` column and rely on the ingestion job to filter out-of-range values before writing.
   • CAdd a CHECK constraint on the table with `heart_rate BETWEEN 30 AND 220` and use a separate quarantine table for rejected rows.
   • DUse a view that filters `heart_rate BETWEEN 30 AND 220` and have all downstream consumers query the view instead of the table.
   Reveal Solution Discussion Select an answer first
6. 20application · medium

   A media streaming company has a `user_events` table with a fixed schema: `event_id`, `user_id`, `event_type`, and `timestamp`. They have noticed that some upstream producers occasionally send extra columns or change the data type of `event_type` from string to integer. The team wants to ensure that the table schema remains consistent and that any data that does not conform is rejected. What should they configure?

   • AEnable Delta Lake schema enforcement on the table, which will reject writes that do not match the existing schema.
   • BUse a Lakeflow expectation that `event_type` is a string and `event_id` is not null, with `onViolation = 'warn'`.
   • CCreate a view that casts `event_type` to string and drops extra columns before reading the table.
   • DSet `spark.sql.legacy.allowCreatingManagedTableUsingNonemptyLocation` to true to allow schema changes.
   Reveal Solution Discussion Select an answer first