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 2)

Questions 6–10

1. 6application · medium

   A financial services firm uses Lakeflow Connect to ingest JSON transaction data into a Delta table. The source schema has changed: a new field 'currency' was added, and an existing field 'amount' changed from integer to double. The team wants the ingestion to continue without manual table alterations. What should they configure?

   • ASet the target table to 'overwriteSchema' to replace the schema with each batch.
   • BDisable schema inference and manually update the table schema after each change.
   • CEnable schema evolution and set 'mergeSchema' to true on the target table.
   • DUse 'failOnSchemaMismatch' to stop ingestion until the schema is fixed.
   Reveal Solution Discussion Select an answer first
2. 7application · medium

   A data engineer is validating a Lakeflow Connect ingestion pipeline that loads JSON data into a Delta table. They want to ensure that the number of records ingested matches the number of records in the source files. What is the best way to validate this?

   • ATrust the Lakeflow Connect job status as successful.
   • BCheck the file size of the Delta table against the source files.
   • CRun a sample query on the Delta table to see if data looks correct.
   • DCompare the row count in the Delta table with the total number of JSON records in the source files.
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4. 8foundation · easy

   What is the primary purpose of Lakeflow Connect in Databricks?

   • ATo manage access control policies for Unity Catalog tables
   • BTo provide a managed service for ingesting data from external sources into Unity Catalog-governed Delta tables
   • CTo transform data using Spark SQL after it has been loaded into Delta tables
   • DTo provide a graphical interface for manually uploading files to the Databricks workspace
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5. 9application · medium

   A data team is using Lakeflow Connect to ingest JSON files into a Delta table. They notice that the schema inference is producing incorrect data types for some fields (e.g., a numeric field is inferred as string). What is the best way to handle this?

   • AIgnore the issue and proceed with the inferred schema.
   • BDefine an explicit schema for the JSON files before ingestion.
   • CRely on schema inference and manually fix the data types after ingestion.
   • DUse 'mergeSchema' to automatically correct data types.
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6. 10foundation · easy

   When configuring a Lakeflow Connect ingestion pipeline, which of the following is a required destination for the ingested data?

   • AA Unity Catalog-registered Delta table
   • BAn external Apache Hive metastore
   • CA Parquet file in the Databricks Filesystem (DBFS)
   • DA temporary view in the Spark session
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