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A team wants to test an experimental ETL change without modifying the shared main branch used by production.
Which acceptance result most directly demonstrates that the intended solution works?
Feature branches isolate work-in-progress revisions from the main release line.
The deployed notebook commit matches the approved remote main-branch revision. That finding does not establish that the main branch retains its original notebook content until the reviewed change is merged.
Staging writes to staging resources while production writes to its independently governed targets. That finding does not establish that the main branch retains its original notebook content until the reviewed change is merged.
A new job run appears for the intended deployed resource and target environment. That finding does not establish that the main branch retains its original notebook content until the reviewed change is merged.
After a pull request is merged, a long-lived Databricks Git folder still shows older notebook code and deployment uses the stale version.
Which implementation decision best addresses this requirement?
A workspace Git folder must reflect the remote revision intended for release. The selected implementation is the one directly aligned with the constraints in the scenario.
Target-specific configuration allows the same codebase to deploy to different workspaces safely.
Bundle validation checks configuration resolution and catches many definition issues before deployment.
Bundles describe a complete deployable project, including resources and environment settings—not only code archives.
After a pull request is merged, a long-lived Databricks Git folder still shows older notebook code and deployment uses the stale version.
Which acceptance result most directly demonstrates that the intended solution works?
A workspace Git folder must reflect the remote revision intended for release.
The production target deploys to its approved host and does not modify development assets. That finding does not establish that the deployed notebook commit matches the approved remote main-branch revision.
An invalid resource reference causes validation to report an error without creating the target job. That finding does not establish that the deployed notebook commit matches the approved remote main-branch revision.
The bundle validates and deploys both its source logic and job resources from the project folder. That finding does not establish that the deployed notebook commit matches the approved remote main-branch revision.
A Declarative Automation Bundle deploys correctly to development but fails in production because the production workspace URL was hardcoded to the development host.
Which implementation decision best addresses this requirement?
Target-specific configuration allows the same codebase to deploy to different workspaces safely. The selected implementation is the one directly aligned with the constraints in the scenario.
Environment variables keep source code common while allowing controlled resource and configuration differences.
Validation and deployment are different operations; deploy creates or updates configured Databricks resources.
A top-level bundle definition anchors common configuration and reduces environment-specific duplication.
A Declarative Automation Bundle deploys correctly to development but fails in production because the production workspace URL was hardcoded to the development host.
Which acceptance result most directly demonstrates that the intended solution works?
Target-specific configuration allows the same codebase to deploy to different workspaces safely.
Staging writes to staging resources while production writes to its independently governed targets. That finding does not establish that the production target deploys to its approved host and does not modify development assets.
The configured job resource appears in the target workspace after successful deployment. That finding does not establish that the production target deploys to its approved host and does not modify development assets.
Validation resolves the common project identity for both development and production targets. That finding does not establish that the production target deploys to its approved host and does not modify development assets.
A team wants identical transformation logic in test and production but different catalog names, service identities and warehouse references.
Which implementation decision best addresses this requirement?
Environment variables keep source code common while allowing controlled resource and configuration differences. The selected implementation is the one directly aligned with the constraints in the scenario.
Bundle validation checks configuration resolution and catches many definition issues before deployment.
The bundle CLI can execute a deployed resource by its logical bundle configuration name.
Non-personal identity avoids tying production deployments to an engineer's account and supports least privilege.
A team wants identical transformation logic in test and production but different catalog names, service identities and warehouse references.
Which acceptance result most directly demonstrates that the intended solution works?
Environment variables keep source code common while allowing controlled resource and configuration differences.
An invalid resource reference causes validation to report an error without creating the target job. That finding does not establish that staging writes to staging resources while production writes to its independently governed targets.
A new job run appears for the intended deployed resource and target environment. That finding does not establish that staging writes to staging resources while production writes to its independently governed targets.
The CI pipeline deploys under a dedicated identity and still works after the original developer loses workspace access. That finding does not establish that staging writes to staging resources while production writes to its independently governed targets.
A developer needs to validate a Databricks bundle configuration before making any changes to workspace resources.
Which implementation decision best addresses this requirement?
Bundle validation checks configuration resolution and catches many definition issues before deployment. The selected implementation is the one directly aligned with the constraints in the scenario.
Validation and deployment are different operations; deploy creates or updates configured Databricks resources.
Bundles describe a complete deployable project, including resources and environment settings—not only code archives.
Validation in CI catches structural defects before resource mutation and reduces broken deployments.
A developer needs to validate a Databricks bundle configuration before making any changes to workspace resources.
Which acceptance result most directly demonstrates that the intended solution works?
Bundle validation checks configuration resolution and catches many definition issues before deployment.
The configured job resource appears in the target workspace after successful deployment. That finding does not establish that an invalid resource reference causes validation to report an error without creating the target job.
The bundle validates and deploys both its source logic and job resources from the project folder. That finding does not establish that an invalid resource reference causes validation to report an error without creating the target job.
A deliberately invalid bundle stops the workflow before the deploy stage. That finding does not establish that an invalid resource reference causes validation to report an error without creating the target job.
A team has validated a bundle but assumes that alone will create its jobs in the workspace.
Which implementation decision best addresses this requirement?
Validation and deployment are different operations; deploy creates or updates configured Databricks resources. The selected implementation is the one directly aligned with the constraints in the scenario.
The bundle CLI can execute a deployed resource by its logical bundle configuration name.
A top-level bundle definition anchors common configuration and reduces environment-specific duplication.
Resource-as-code deployment promotes job behavior along with executable logic.
A team has validated a bundle but assumes that alone will create its jobs in the workspace.
Which acceptance result most directly demonstrates that the intended solution works?
Validation and deployment are different operations; deploy creates or updates configured Databricks resources.
A new job run appears for the intended deployed resource and target environment. That finding does not establish that the configured job resource appears in the target workspace after successful deployment.
Validation resolves the common project identity for both development and production targets. That finding does not establish that the configured job resource appears in the target workspace after successful deployment.
Production contains the required schedule and DAG after deployment, not just the notebook source. That finding does not establish that the configured job resource appears in the target workspace after successful deployment.
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