Implement and Manage Data Quality Constraints in Unity Catalog DP-750 Practice Questions (Page 1)

Questions 1–5

1. 1application · medium

   A data engineering team uses Lakeflow Spark Declarative Pipelines to process sensor data. They need to define a data quality expectation that ensures the `temperature` column is not null and is between -50 and 150. They want the pipeline to quarantine invalid rows instead of failing the entire run. What is the correct way to define this expectation?

   • AUse `filter` to remove invalid rows before writing to the table.
   • BUse `expect_all` with separate conditions for null and range checks.
   • CUse `expect` with the same condition and set the action to `fail`.
   • DUse `expect` with `temperature IS NOT NULL AND temperature BETWEEN -50 AND 150` and set the action to `warn`.
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2. 2application · medium

   A data engineering team is building a Lakeflow pipeline that ingests sensor readings. They want to enforce that `temperature` is between -50 and 150, `sensor_id` is not null, and they want the pipeline to stop if any batch violates these rules. How should they configure the pipeline?

   • AAdd a CHECK constraint on the target table and set `onViolation` to 'fail' in the pipeline.
   • BUse a `FILTER` transformation in the pipeline to remove rows that do not meet the conditions.
   • CSet the table property `delta.autoOptimize.optimizeWrite = true` to improve write performance.
   • DDefine an expectation in the pipeline with `temperature BETWEEN -50 AND 150 AND sensor_id IS NOT NULL` and set `onViolation` to 'fail'.
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4. 3foundation · easy

   Which Delta table feature is enabled by default and prevents writes that do not match the table schema?

   • AChange data feed
   • BTime travel
   • CSchema evolution
   • DSchema enforcement
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5. 4foundation · easy

   In a Delta table, which SQL constraint can be used to enforce that a column's values are between 0 and 100?

   • APRIMARY KEY constraint
   • BUNIQUE constraint
   • CNOT NULL constraint
   • DCHECK constraint
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6. 5expert · hard

   A data platform ingests user activity logs. The `event_time` column is expected to be a timestamp, but some sources send it as a string. The team wants to standardize `event_time` to a timestamp in the target table, and they also want to ensure that `user_id` is not null. They are using a Lakeflow pipeline and want to handle schema changes gracefully. What should they do?

   • AUse a transformation that casts `event_time` to timestamp and add an expectation that `user_id` is not null, with `onViolation = 'fail'`.
   • BUse a generated column to convert `event_time` to timestamp and fill null `user_id` with a default value.
   • CSet the table property `delta.columnMapping.mode` to 'name' and rely on it to handle type changes.
   • DAdd a CHECK constraint on `event_time` to enforce it is a timestamp and `user_id` is not null.
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