Promoting one codebase across dev, test, and prod with bundle variables and overrides
Databricks Asset Bundles let you define one codebase and deploy it consistently across dev, test, and prod using a single databricks.yml with target-specific overrides. Variables parameterize values like cluster sizes, paths, and job schedules so each target gets appropriate settings without duplicating code. This keeps environment differences declarative and version-controlled rather than hardcoded.
Must-know
- Bundle configuration is defined in databricks.yml, with a top-level variables block declaring defaults and a targets block (e.g., dev, staging, prod) that can override those variables or resource settings per environment.
- The mode: development setting on a target enables features like resource name prefixing and auto-pausing schedules to prevent dev runs from colliding with production resources.
- Variables can be overridden via the CLI at deploy time using --var, via environment variables, or in the target-specific variables block, with CLI/env taking precedence over file defaults.
- Each target typically maps to a distinct workspace (via host) and
run_asidentity, so the same job/pipeline definitions deploy to physically separate dev/test/prod workspaces or paths. - Complex variables (maps/objects, e.g., cluster spec overrides) can be overridden per target, not just simple strings, allowing structural differences like node types or autoscaling between environments.
- Running databricks bundle validate before deploy catches misconfigured variable references or target overrides early, and databricks bundle deploy -t <target> deploys to the specified environment explicitly.
A data engineering team maintains one Databricks Asset Bundle repository deployed to dev, test, and prod. The prod target must run on a job cluster with 8 workers and write to catalog prod_sales, while dev must use a single-node cluster and write to dev_sales. The team wants one databricks.yml file. Which configuration approach meets these requirements?
What you have tried across Databricks DEA's objectives, not a readiness score.
Databricks Intelligence Platform
Data Ingestion and Loading
- Batch, streaming, and incremental loading patterns, and where the data comes from
- Loading files from cloud storage into governed tables with COPY INTO
- Landing data with Auto Loader, and handling schema enforcement and evolution
- Setting up Lakeflow Connect to ingest from enterprise sources reliably
- Pulling data through JDBC, ODBC, or REST clients and scheduling the job
- Choosing the right ingestion method for a given volume, frequency, and governance need
- Bringing semi-structured and unstructured data into governed Delta tables
Data Transformation and Modeling
- Cleaning bronze data into silver tables with PySpark and SQL
- Joining and combining DataFrames with the different join and union types
- Reshaping columns, rows, and arrays in a table
- Deduplicating and aggregating DataFrames
- Tuning Spark's core parameters and measuring what changed
- Building Gold-layer views and tables for BI and analytics
- Validating Silver and Gold datasets for quality
Working with Lakeflow Jobs
Implementing CI/CD
- Branching, committing, and opening pull requests from inside the Databricks workspace
- Promoting one codebase across dev, test, and prod with bundle variables and overrides
- Packaging and deploying jobs and pipelines with Automation Bundles
- Validating and managing bundle deployments from the Databricks CLI
Troubleshooting, Monitoring, and Optimization
- Spotting performance trends in a job's run history
- Reading job status, task graphs, and failure rates to monitor pipeline health
- Diagnosing skew, shuffle, and spill from Spark UI stage metrics
- What Liquid Clustering and predictive optimization actually do
- Diagnosing cluster startup failures, library conflicts, and out-of-memory errors
Governance and Security
Coverage checked against the published exam guide on Jul 26, 2026.
These are independent practice questions, written against this certification's published exam guide. They are not the certification vendor's own questions, and not the real exam.