Validating and managing bundle deployments from the Databricks CLI
The Databricks CLI is the primary tool for automating deployment of Declarative Automation Bundles (formerly Databricks Asset Bundles) in CI/CD pipelines, letting teams validate, deploy, and run workspace assets like jobs, pipelines, and notebooks from source-controlled YAML config. Bundles package code and infra config together so environments (dev, staging, prod) deploy consistently via CLI commands rather than manual UI clicks.
Must-know
- A bundle is defined by a databricks.yml file at its root, specifying resources (jobs, pipelines), targets (environments), and artifacts.
- Core CLI lifecycle commands are
databricks bundle validate(checks config syntax/schema before deploy),databricks bundle deploy(pushes resources to a target workspace), anddatabricks bundle run(executes a deployed resource). - Bundles support multiple targets (e.g., dev/staging/prod) in one YAML file, each with its own workspace host, mode (development vs production), and resource overrides.
databricks bundle destroytears down all resources associated with a deployed target, useful for cleaning up ephemeral CI environments.- Authentication for CLI-driven CI/CD typically uses a Databricks personal access token or OAuth/service principal credentials stored as pipeline secrets, not interactive login.
- The CLI is a single self-contained binary (no Python dependency required) making it straightforward to install in CI runners like GitHub Actions or Azure DevOps.
A data engineer has written a Databricks Asset Bundle configuration (databricks.yml) defining a job and wants to check for syntax errors and resolve variable references before deploying to any environment. Which CLI command should be run first?
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.