Implementing CI/CD

Which of the following is a fundamental principle of implementing effective CI/CD for Databricks workflows?

A CI/CD pipeline uses the Databricks CLI to deploy a job to production. The pipeline must ensure that the job configuration is identical across environments except for the cluster size, which differs between staging and production. Which approach best supports this requirement?

A data engineering team uses Databricks Asset Bundles (DABs) to manage a job that must deploy to both a staging and a production workspace. The team wants to avoid hardcoding workspace-specific values such as the cluster ID and the storage path in databricks.yml. Which approach should they use?

A platform team is setting up a CI/CD pipeline that deploys Databricks jobs and notebooks from a Git repository. They must ensure deployments are secure and auditable. (Choose two.)

A team is setting up a CI/CD pipeline that deploys Databricks Asset Bundles to production. They want the pipeline to be secure and to fail fast before any production resources are changed. Which TWO practices should they implement? (Choose two.)

When promoting code from a development workspace to a production workspace, what is the primary risk of using manual notebook exports?

What is the primary purpose of a 'feature branch' in a Git-based workflow for Databricks?

A data engineering team is setting up a CI/CD pipeline for Databricks notebooks using Databricks Repos and a Git provider. They want to ensure that changes are tested before being merged and that production deployments are controlled. Which TWO practices should they implement? (Choose two.)

A team is designing a CI/CD pipeline using Databricks Asset Bundles (DABs). Which TWO of the following are primary benefits of using DABs for managing Databricks projects?

Refer to the exhibit. A CI/CD pipeline running a Databricks CLI command fails with the error shown. What is the most likely cause?

A data platform team is migrating their deployment process to Databricks Asset Bundles (DABs). They already have a Python wheel task defined in a Databricks Job and a set of notebooks in a Git repository. They want the bundle deployment to be repeatable across development, staging, and production targets with different cluster sizes. Which approach should they take to parameterize the target-specific cluster configuration?

A data engineering team stores all production notebooks and job definitions in a Git repository. They want every merge to the `main` branch to automatically deploy the updated notebooks to the production Databricks workspace without any manual copy/paste. Which approach should they implement?

Which component of Databricks CI/CD is responsible for executing automated tests on code before it is merged into the main branch?

A team is implementing a CI/CD process for their Delta Live Tables (DLT) pipelines. Which THREE of the following practices are recommended to ensure reliable deployment?

Refer to the exhibit. A CI/CD pipeline fails with the provided error. What is the most likely cause?

A team wants to automate deployment of Databricks jobs from a Git repository using a CI/CD pipeline. They need a tool that reads a declarative project definition and creates or updates jobs, pipelines, and notebooks in a target workspace. Which Databricks capability should they use?

Which TWO of the following statements are correct regarding the use of Databricks Repos for CI/CD?

A data engineering team stores its Databricks notebooks and Python files in a Git repository. They want to avoid manually copying files into the workspace and ensure that the production workspace always runs the exact code version that passed tests. Which approach should they use?

A data engineering team stores notebooks in a Git repository and wants automated deployments to a Databricks workspace. They configure a GitHub Actions workflow that runs a Databricks CLI command to deploy Databricks Asset Bundles. The workflow authenticates using a service principal OAuth token stored in GitHub Secrets. After the first successful run, subsequent runs fail with an authentication error. The token was created with a 1-hour lifetime. What should the team do to ensure the workflow can authenticate reliably on every run?

A team is using Databricks Asset Bundles (DABs) to manage their CI/CD pipeline. They want to run unit tests on their Python code before deploying the bundle. Where should the tests be executed in the pipeline?

Why is it important to use Service Principals instead of Personal Access Tokens (PATs) for CI/CD automation in Databricks?

Which of the following is a recommended strategy for managing library dependencies in a CI/CD pipeline for Databricks?

A data engineering team is implementing a CI/CD pipeline for Databricks notebooks and jobs using Databricks Asset Bundles. They want to ensure deployments are reproducible and that production changes are traceable. Which TWO of the following practices should they follow? (Choose two.)

What is the primary goal of implementing 'environment parity' in a Databricks CI/CD pipeline?

A team is using the Databricks CLI in their CI/CD pipeline to deploy jobs and notebooks. They need the pipeline to authenticate to a production workspace without embedding a personal user's credentials. Which authentication method should they configure for the CLI?

A data engineering team wants to implement Git integration for their Databricks notebooks. Which workflow is considered the best practice for CI/CD in Databricks Repos?

A data engineer is setting up a CI/CD pipeline that runs unit tests on transformation logic before deploying notebooks to a production Databricks workspace. The tests must run quickly and not depend on a live Databricks cluster or external data sources. Which approach best meets these requirements?

A data engineering manager wants to ensure that all production code in Databricks is fully audited and versioned. Which TWO of the following steps are required?

A team uses Databricks Asset Bundles to define a job that must exist in both a staging and a production workspace with different cluster sizes. They want a single bundle definition that deploys correctly to both targets. Which configuration should they use?

Your team is migrating a manual Databricks job to a CI/CD pipeline. You need to ensure the job configuration is version-controlled and deployed programmatically. Which approach aligns with Databricks best practices?

A team is using Databricks Asset Bundles (DABs) to deploy a job to multiple environments (dev, staging, prod). They need to ensure that the job uses different cluster sizes and schedules per environment. Which DABs feature should they use?

A data engineer is configuring a CI/CD pipeline for Databricks notebooks using GitHub Actions. They need to authenticate to Databricks to deploy notebooks. Which authentication method is recommended for production CI/CD pipelines?

A team stores Databricks notebooks and job definitions in a Git repository and wants every merge to the main branch to automatically deploy to production. Which combination of practices should the pipeline implement to achieve this safely?

When utilizing Databricks Asset Bundles, how should secrets (e.g., API keys, database credentials) be handled to ensure security during CI/CD?

Why should developers avoid using 'notebook' references in production pipelines that point to the 'Shared' folder for shared development work?

When integrating Databricks with a CI/CD tool like GitHub Actions, how should you securely manage the authentication token used for deployments?

A CI/CD pipeline must run unit tests on Python transformation code before deploying a Databricks job. The tests should execute quickly without starting a cluster and should validate the transformation logic in isolation. Which approach best meets these requirements?

A team is using Databricks Asset Bundles to manage a job that writes to a Unity Catalog table. The bundle is deployed to a staging workspace for testing and then to a production workspace. The team wants the job to use different catalog and schema names in each environment without duplicating the entire bundle. Which approach should they use?

A team wants to ensure that code changes to their Databricks notebooks are reviewed before being deployed to production. They use a Git repository and Databricks Repos. Which practice should they implement in their CI/CD process?

A CI/CD pipeline runs unit tests against transformation logic before deploying to production. The tests must run on a Databricks cluster and produce a pass/fail result that fails the pipeline when assertions do not hold. Which implementation best fits this requirement?