Branching, committing, and opening pull requests from inside the Databricks workspace
Databricks Repos syncs a folder in your workspace with a remote Git repository, letting you manage branches, commits, and pull requests without leaving the Databricks UI. This supports standard Git workflows so notebooks and code can be version-controlled alongside CI/CD pipelines.
Must-know
- You must connect a Git provider (e.g., GitHub, GitLab, Bitbucket, Azure DevOps) via personal access token in User Settings before cloning a repo into Databricks Repos.
- The branch selector in the Repos UI lets you create a new branch from an existing one and switch branches directly, checking out the latest commit for that branch.
- Changes to notebooks and files in a repo are tracked as uncommitted changes; you use the built-in dialog to stage, commit with a message, and push to the remote.
- Databricks Repos does not support merging branches directly in the UI; merges and pull requests are completed on the Git provider's site, though Databricks provides a link/button to open the PR creation page.
- Pulling changes from the remote updates the workspace copy, and conflicts must be resolved in the Git provider (or locally), since Databricks Repos has no built-in merge conflict resolution tool.
- Notebooks are stored in source format (e.g., .py, .sql, .ipynb) in Repos so diffs are readable in Git, unlike the default Databricks notebook (.dbc) format.
A data engineer is working in a Databricks Git folder that is currently checked out on the main branch. Before editing a notebook to add a new transformation step, the engineer wants to isolate the change so main stays deployable. Which action should the engineer take 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 27, 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.