Understand Environment-Specific Configuration Using Automation Bundle (formerly Databricks Asset Bundles) Variables and Overrides While Promoting the Same Codebase Across Dev, Test, and Prod Targets. DATA-ENGINEER-ASSOCIATE Practice Questions (Page 2)

Questions 6–10

1. 6foundation · easy

   In a Databricks Asset Bundle, where do you define environment-specific overrides for variables?

   • AIn a separate file for each environment, such as dev.yml, test.yml, and prod.yml
   • BIn the variables section of databricks.yml using the 'default' key
   • CIn the targets section of databricks.yml under each target name
   • DIn the bundle section of databricks.yml using the 'overrides' key
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2. 7application · medium

   A data engineering team maintains a Databricks Asset Bundle for a batch pipeline. The bundle is deployed to dev, test, and prod targets. The dev cluster uses a small instance type (Standard_DS3_v2) while prod requires a larger type (Standard_DS5_v2). The team wants to define the instance type once in the bundle and have it automatically resolve to the correct value per target without requiring CLI flags. Which configuration approach should they use?

   • AUse a single variable with no default and rely on the CLI to prompt for the value
   • BHardcode the prod instance type in the bundle's resources section and use a separate bundle for dev
   • CDefine the instance type as a variable and pass it via `--var` flag during every deployment
   • DDefine a variable `cluster_instance_type` with a default value, then override it in each target's `databricks.yml` section
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4. 8expert · hard

   A team has a Databricks Asset Bundle with a variable `timeout_seconds` defined as follows: the default is 3600; the `dev` target overrides it to 600; the `test` target overrides it to 1800; the `prod` target has no override. A CI/CD pipeline runs `databricks bundle deploy --target prod --var timeout_seconds=7200`. What is the final value of `timeout_seconds` for the prod deployment?

   • A1800
   • B600
   • C3600
   • D7200
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5. 9application · medium

   A company uses a Databricks Asset Bundle to deploy a job to dev and prod. The job's cluster uses a `spark_version` that must be different in dev (12.2.x-scala2.12) and prod (13.3.x-scala2.12). The team wants to define the Spark version once and have it resolve correctly per target. What should they do?

   • AHardcode the prod Spark version in the cluster definition and use a separate bundle for dev
   • BUse a single Spark version for both environments
   • CDefine a variable `spark_version` and override it in each target's configuration
   • DSet the Spark version in the notebook code using a Python `if` statement
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6. 10application · medium

   A data engineering team uses a Databricks Asset Bundle to deploy a pipeline. The pipeline's notebook references a database name. The team wants to make the database name configurable per environment (dev, test, prod) without changing the notebook code. What is the best way to achieve this?

   • AHardcode the database name in the notebook and use a `if` statement to switch per environment
   • BCreate a separate notebook for each environment
   • CUse a global variable in the cluster's Spark configuration
   • DDefine a bundle variable `database_name` and reference it in the notebook using `{{var.database_name}}`
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