Apply Data Quality Checks and Validation Rules to Ensure Reliable Silver and Gold Datasets. DATA-ENGINEER-ASSOCIATE Practice Questions (Page 1)

Questions 1–5

1. 1application · medium

   A data platform team has implemented Delta Live Tables pipelines for Silver and Gold layers. They want to be notified when the percentage of records failing data quality checks exceeds a threshold, so they can investigate before the issue escalates. What should they do?

   • AUse a Delta Live Tables expectation with `onViolation='fail'` to stop the pipeline on any violation.
   • BSet up a Databricks SQL alert on a query that monitors the Delta Live Tables event log for expectation failures.
   • CConfigure the pipeline to send an email every time any record fails a check.
   • DManually check the pipeline run history each day.
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2. 2foundation · easy

   A Gold table used for executive reporting must contain every sales transaction from the source system, with no missing rows. Which data quality dimension is the primary focus?

   • ACompleteness
   • BValidity
   • CConsistency
   • DUniqueness
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4. 3foundation · easy

   A data engineer wants to add a validation rule in a PySpark DataFrame transformation that fails the job if any row has a null in the 'amount' column. Which approach directly implements this check?

   • Adf.fillna(0, subset=['amount'])
   • Bdf.dropna(subset=['amount'])
   • Cassert df.filter(col('amount').isNull()).count() == 0, 'Null amount found'
   • Ddf.filter(col('amount').isNull()).count() == 0
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5. 4foundation · easy

   A data engineer is processing a Silver table where a small number of records fail a validation rule. The business wants to keep those records for later analysis but does not want them to appear in the Gold table. Which strategy best meets this requirement?

   • AInclude invalid records in the Gold table
   • BDrop invalid records permanently
   • CFail the pipeline
   • DQuarantine invalid records to a separate table
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6. 5application · medium

   A data team wants to monitor the timeliness of their Gold layer tables. They need to ensure that the data is refreshed within 24 hours of the source system update. Which data quality dimension and monitoring approach should they use?

   • ACheck timeliness by comparing the maximum timestamp in the Gold table to the current time.
   • BCheck accuracy by comparing values to a trusted source.
   • CCheck uniqueness by ensuring no duplicate records exist.
   • DCheck completeness by verifying that all expected rows are present.
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