Ingest Semi-Structured and Unstructured Data (for Example, JSON and Nested Data) via Lakeflow Connect and Other Managed Connectors into UnityCatalog–governed Delta Tables. DATA-ENGINEER-ASSOCIATE Practice Questions (Page 5)

Questions 21–25

1. 21application · medium

   A logistics company ingests shipment tracking data as nested JSON. Each record contains a 'shipment' object with an 'events' array. The analytics team needs a Delta table where each event is a separate row, with shipment-level fields repeated. Which approach should the data engineer use?

   • AUse 'collect_list' to aggregate all events into a single array per shipment.
   • BUse the 'to_json' function to convert the entire record into a single string column.
   • CUse the 'from_json' function to parse the JSON and then apply 'explode' on the 'events' array.
   • DUse 'regexp_extract' to pull out each event from the raw JSON string.
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2. 22foundation · easy

   When writing data to a Unity Catalog-governed Delta table, which of the following must be specified?

   • AThe file format of the underlying data (e.g., Parquet, ORC)
   • BThe full three-level namespace: catalog.schema.table
   • CThe storage location in DBFS
   • DThe cluster ID of the compute resource
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4. 23expert · hard

   A data engineer is troubleshooting a Lakeflow Connect ingestion job that loads JSON files into a Delta table. The job succeeds, but the row count in the table is lower than expected. The engineer suspects that some records are being silently dropped. What is the best way to identify the dropped records?

   • ACompare the file sizes of the source and the Delta table.
   • BRun a sample query on the Delta table to see if data looks correct.
   • CEnable the 'rescuedDataColumn' option to capture records that don't match the schema.
   • DCheck the job logs for any warnings.
   Reveal Solution Discussion Select an answer first
5. 24foundation · easy

   Which of the following is a common technique to validate data quality during ingestion?

   • AUsing the Databricks CLI to validate data
   • BUsing the Delta table's version history to validate data
   • CUsing Unity Catalog to automatically clean invalid data
   • DUsing Delta Live Tables expectations to enforce data quality constraints
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6. 25expert · hard

   A data engineer needs to flatten a deeply nested JSON structure into a Delta table. The JSON contains arrays of objects, and some fields are missing in some records. The engineer wants to ensure that the resulting table has consistent columns, with nulls for missing fields. What is the best approach?

   • AUse 'to_json' to convert the entire JSON to a string and then parse it manually.
   • BUse 'from_json' with an explicit schema that includes all possible fields, then use 'explode' on arrays.
   • CUse 'schema_of_json' to infer the schema and then use 'from_json'.
   • DUse 'regexp_extract' to pull out each field from the raw JSON string.
   Reveal Solution Discussion Select an answer first