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 1)

Questions 1–5

1. 1application · medium

   A company uses a Databricks Asset Bundle to deploy a job to dev and prod. The job's cluster uses a `node_type_id` that must be different in dev (Standard_DS3_v2) and prod (Standard_DS5_v2). The team wants to define the node type once and have it resolve correctly per target. What should they do?

   • AUse a single node type for both environments
   • BSet the node type in the notebook code using a Python `if` statement
   • CDefine a variable `node_type_id` and override it in each target's configuration
   • DHardcode the prod node type in the cluster definition and use a separate bundle for dev
   Reveal Solution Discussion Select an answer first
2. 2foundation · easy

   What is the primary benefit of using a single Databricks Asset Bundle codebase for multiple environments?

   • AIt allows you to deploy to multiple environments in parallel
   • BIt automatically scales resources in each environment
   • CIt ensures that the same code is deployed to all environments, reducing drift
   • DIt eliminates the need for testing in lower environments
   Reveal Solution Discussion Select an answer first
4. 3foundation · easy

   What is the main configuration file for a Databricks Asset Bundle?

   • Adatabricks.yml
   • Bdeployment.yml
   • Cbundle.yml
   • Dconfig.yml
   Reveal Solution Discussion Select an answer first
5. 4foundation · easy

   Where are environment-specific overrides typically defined in a Databricks Asset Bundle configuration?

   • AIn the 'variables' section of databricks.yml
   • BIn a separate 'overrides.yml' file
   • CIn the 'targets' section of databricks.yml
   • DIn the 'resources' section of databricks.yml
   Reveal Solution Discussion Select an answer first
6. 5application · medium

   A company promotes a Databricks Asset Bundle from dev to test to prod. The bundle contains a job that writes to a table. In dev, the table is in the `dev_catalog`; in test, it is in `test_catalog`; in prod, it is in `prod_catalog`. The team wants to ensure the same notebook code is used across all environments. What should they do?

   • ASet the catalog name in the cluster's Spark configuration and reference it as a global variable
   • BHardcode the prod catalog in the notebook and use environment variables in the code
   • CUse a different notebook for each environment with the catalog hardcoded
   • DDefine a variable `catalog_name` and reference it in the notebook, then override it per target
   Reveal Solution Discussion Select an answer first