A data engineering team uses Declarative Automation Bundles, formerly known as Databricks Asset Bundles, to deploy the same codebase across development, test, and production environments. The team wants environment-specific behavior to be applied only through bundle configuration, without modifying notebooks, job definitions, or deployment logic during promotion.
Which approach applies environment-specific configuration while meeting this requirement?

• A.Define bundle variables and reference them from bundle resources, and then override the variable values for each environment using bundle targets.
• B.Deploy the same bundle once and edit job settings in the workspace UI for each environment after deployment.
• C.Parameterize environment-specific values inside notebooks and set those parameters whenever each job run is triggered.
• D.Use separate Git branches for each environment so each branch can contain environment-specific configuration values.

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Which Databricks Asset Bundle format is valid?

• A.resources:
  jobs:
  hello-job:
  name: hello-job
  tasks:
  - task_key: hello-task
  existing_cluster_id: 1234-567890-abcde123
  notebook_task:
  notebook_path: ./hello.py
• B.{
  " resources " : {
  " jobs " : {
  " name " : " hello-job " ,
  " tasks " : {
  " task_key " : " hello-task " ,
  " existing_cluster_id " : " 1234-567890-abcde123 " ,
  " notebook_task " : {
  " notebook_path " : " ./hello.py "
  }
  }
  }
  }
  }
• C.configuration = {
  " resources " : {
  " jobs " : {
  " name " : " hello-job " ,
  " tasks " : {
  " task_key " : " hello-task " ,
  " existing_cluster_id " : " 1234-567890-abcde123 " ,
  " notebook_task " : {
  " notebook_path " : " ./hello.py "
  }
  }
  }
  }
  }
• D.resources {
  jobs {
  name = " hello-job "
  tasks {
  task_key = " hello-task "
  existing_cluster_id = " 1234-567890-abcde123 "
  notebook_task {
  notebook_path = " ./hello.py "
  }
  }
  }
  }

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A data engineer has a Job with multiple tasks that runs nightly. Each of the tasks runs slowly because the clusters take a long time to start.
Which of the following actions can the data engineer perform to improve the start up time for the clusters used for the Job?

• A.They can configure the clusters to be single-node
• B.They can configure the clusters to autoscale for larger data sizes
• C.They can use clusters that are from a cluster pool
• D.They can use jobs clusters instead of all-purpose clusters
• E.They can use endpoints available in Databricks SQL

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A data engineering team has two tables. The first table march_transactions is a collection of all retail transactions in the month of March. The second table april_transactions is a collection of all retail transactions in the month of April. There are no duplicate records between the tables.
Which of the following commands should be run to create a new table all_transactions that contains all records from march_transactions and april_transactions without duplicate records?

• A.CREATE TABLE all_transactions ASSELECT * FROM march_transactionsINNER JOIN SELECT * FROM april_transactions;
• B.CREATE TABLE all_transactions ASSELECT * FROM march_transactionsUNION SELECT * FROM april_transactions;
• C.CREATE TABLE all_transactions ASSELECT * FROM march_transactionsOUTER JOIN SELECT * FROM april_transactions;
• D.CREATE TABLE all_transactions ASSELECT * FROM march_transactionsINTERSECT SELECT * from april_transactions;
• E.CREATE TABLE all_transactions ASSELECT * FROM march_transactionsMERGE SELECT * FROM april_transactions;

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A data engineer is standardizing repository layouts for multiple teams adopting Databricks Asset Bundles. The engineer wants to ensure every project has a single authoritative configuration file at the repository root that defines the bundle name, targets, workspace settings, permissions, and resource mappings (for jobs and pipelines).
What strategy should the data engineer use to meet the goal?

• A.Place multiple databricks.ymlfiles under each subfolder (for example, jobs/pipelines/, workspace/) and merge them at deploy time using the include mapping.
• B.Place exactly one databricks.yml at the repository root; it is the main configuration file and may reference additional configuration files via the include mapping.
• C.Place a databricks.ymlin a databricks/hidden folder at the repository root; only hidden locations are valid for bundle configs.
• D.Place a databricks.yaml at the repository root and optional databricks.yml in subfolders; the CLI prefers .yamlover .ymlwhen both exist.

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