Use JDBC/ODBC or REST Clients in Notebooks to Land Data into Cloud Storage or Directly into UnityCatalog–governed Tables, Usually Orchestrated and Scheduled with Lakeflow Jobs. DATA-ENGINEER-ASSOCIATE Practice Questions (Page 4)

Questions 16–20

1. 16foundation · easy

   What is a key benefit of incremental loads over full loads when ingesting data from a JDBC source?

   • AReduced data transfer and processing time
   • BEliminates the need for error handling
   • CGuarantees data is always up-to-date
   • DSimplifies the schema evolution process
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2. 17foundation · easy

   What is a key consideration when designing retry logic for idempotent ingestion?

   • ARetries should be unlimited
   • BThe operation should produce the same result even if repeated
   • CRetries should only be attempted once
   • DThe retry delay should always be zero
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4. 18foundation · easy

   Which Python library is commonly used in Databricks notebooks to make REST API calls?

   • Aos
   • Bnumpy
   • Crequests
   • Dpandas
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5. 19foundation · easy

   After reading data from a JDBC source into a DataFrame, what must be done to query it using Spark SQL in the same notebook?

   • AUse the JDBC connection string in the SQL query
   • BConvert the DataFrame to a Pandas DataFrame
   • CWrite the DataFrame to a Unity Catalog table
   • DCreate a temporary view from the DataFrame
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6. 20foundation · easy

   Which DataFrame writer method is used to write data to cloud storage in a specific format like Parquet?

   • Adf.write.jdbc("s3://bucket/path", "parquet")
   • Bdf.write.format("parquet").save("s3://bucket/path")
   • Cdf.write.parquet("s3://bucket/path")
   • Ddf.write.csv("s3://bucket/path")
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