Using Python and Tools for Development DATA-ENGINEER-PROFESSIONAL Practice Questions (Page 1)

Questions 1–5

1. 1application · medium

   A data engineer is writing a Python UDF to extract the domain from an email address. The UDF uses the `re` module to parse the string. The engineer wants to ensure the UDF handles null values gracefully by returning `null` for null inputs. What is the correct way to define the UDF?

   • ASet the UDF's return type to `StringType()` and rely on Spark to convert null to an empty string.
   • BDefine the UDF to check if the input is `None` and return `None` in that case.
   • CDefine the UDF without null handling, and rely on Spark to skip null rows.
   • DUse `coalesce` on the input column before applying the UDF to replace nulls with an empty string.
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2. 2foundation · easy

   When designing a modular Python project structure for a Databricks Asset Bundle, which directory layout best separates code, configuration, and resources to support deployment automation?

   • ASeparate directories for src, resources, and databricks.yml at the root
   • BA single flat directory containing all Python files, YAML configs, and notebooks
   • CAll code inside a single notebook with embedded YAML configuration
   • DA directory per environment (dev, staging, prod) each containing its own copy of the code
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4. 3application · medium

   A data engineering team uses Databricks Asset Bundles to deploy notebooks and jobs to multiple environments (dev, staging, prod). They want to promote a bundle from staging to production without changing the code. What is the recommended way to do this?

   • ACopy the deployed staging artifacts to the production workspace using `dbutils.fs.cp`.
   • BModify the `databricks.yml` file to change the target to production and redeploy.
   • CRun `databricks bundle deploy --target staging` and then `databricks bundle deploy --target prod` from the same codebase.
   • DUse the same target name for both environments and rely on the workspace path to differentiate.
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5. 4expert · hard

   A data engineer is installing a custom wheel on a Databricks cluster. The wheel was built on a local machine with Python 3.9, but the cluster uses Python 3.10. The installation fails with an error about an invalid wheel format. What is the most likely cause and solution?

   • AThe wheel is not compatible with Python 3.10; rebuild the wheel with Python 3.10.
   • BThe wheel is built for a different operating system; use a platform-specific wheel.
   • CThe wheel is corrupted; re-upload the wheel file.
   • DThe cluster does not have `pip` installed; install pip first.
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6. 5foundation · easy

   In a Databricks Asset Bundle, how are different environments (e.g., dev, staging, prod) typically defined?

   • AAs separate branches in the Git repository
   • BAs separate bundle projects
   • CAs targets in the databricks.yml file
   • DAs environment variables in the shell
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