Apply Data Quality Checks and Validation Rules to Ensure Reliable Silver and Gold Datasets. DATA-ENGINEER-ASSOCIATE Practice Questions (Page 3)

Questions 11–14

1. 11foundation · easy

   A Gold table must only contain order records where the order_amount is between 0 and 100,000. Which validation rule is most appropriate to enforce this requirement?

   • AA referential integrity rule linking order_amount to a lookup table
   • BA schema check that order_amount is a decimal type
   • CA uniqueness constraint on order_amount
   • DA value range check that order_amount is >= 0 and <= 100000
   Reveal Solution Discussion Select an answer first
2. 12expert · medium

   A data platform team has multiple Delta Live Tables pipelines producing Silver and Gold tables. They want to centralize monitoring of data quality metrics across all pipelines and receive alerts when any table's completeness drops below 95%. What is the most effective approach?

   • AUse a single Delta Live Tables pipeline for all tables to simplify monitoring.
   • BConfigure each pipeline to send an email on every run with the completeness metric.
   • CUse the Delta Live Tables event log to collect metrics from all pipelines into a central Delta table and set up a dashboard with alerts.
   • DCreate a separate Databricks SQL alert for each table's completeness.
   Reveal Solution Discussion Select an answer first
4. 13foundation · easy

   A data engineer wants to ensure that every order_id in a Silver orders table is unique and that no null values exist in the order_id column. Which validation rule best captures this requirement?

   • AA null check that rejects rows where order_id is null
   • BA uniqueness constraint on order_id combined with a not-null constraint
   • CA referential integrity rule that checks order_id exists in the customers table
   • DA schema check that verifies the order_id column is of type integer
   Reveal Solution Discussion Select an answer first
5. 14application · medium

   A financial services company loads transaction data into a Silver table. A small percentage of records fail a referential integrity check because they reference customer IDs that do not exist in the customer dimension. The business analysts need these transactions for reporting, but they must be clearly identifiable as invalid. The pipeline should not fail. What is the best strategy?

   • AIgnore the check and write all records to Silver.
   • BFail the pipeline when the referential integrity check fails.
   • CWrite the invalid records to a separate quarantine table and continue the pipeline.
   • DDrop the invalid records before writing to Silver.
   Reveal Solution Discussion Select an answer first