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 6)

Questions 26–29

1. 26foundation · easy

   When troubleshooting an ingestion pipeline that is failing to load JSON files, which of the following is a common issue to check?

   • AThe number of nodes in the cluster
   • BThe table's version history
   • CThe color of the cluster's Spark UI
   • DMalformed JSON records that cannot be parsed
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2. 27foundation · easy

   Which of the following is a managed connector that can be used to ingest data from an external database into a Delta table?

   • AUnity Catalog metastore
   • BDBFS mount point
   • CDatabricks SQL endpoint
   • DJDBC connector
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4. 28application · medium

   A data engineer is working with a JSON file that contains a nested structure like: {"user": {"name": "Alice", "address": {"city": "NYC"}}}. They need to create a Delta table with columns 'user_name' and 'user_address_city'. Which Spark function should they use to extract these nested fields?

   • AUse 'to_json' to convert the JSON to a string and then parse it manually.
   • BUse 'regexp_extract' to pull out the fields from the raw JSON string.
   • CUse 'from_json' to parse the JSON and then access nested fields using dot notation.
   • DUse 'split' on the JSON string to separate fields.
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5. 29foundation · easy

   What is the purpose of the `from_json` function in Spark when working with JSON data?

   • ATo load JSON files from cloud storage
   • BTo flatten nested JSON into multiple columns
   • CTo convert a DataFrame to a JSON string
   • DTo parse a JSON string into a structured column (e.g., struct or array)
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