Exam Bundle

Discoverability surfaces show the asset's approved trusted status would demonstrate the separate action Apply a supported certification/quality signal after governance review, which addresses “A dataset should be marked as validated for enterprise reuse.” It does not verify the requirement “An audit requires a documented retention policy for a catalog table” following Record and enforce the approved retention and lifecycle metadata.

That observation is a valid indicator for “An outdated table should no longer be suggested for new reports” when using Mark the object as deprecated and point users to replacement data. The scenario instead tests “An audit requires a documented retention policy for a catalog table”; the relevant result is Owners can verify both documented retention and actual cleanup jobs.

That observation is a valid indicator for “Different departments calculate net revenue using inconsistent formulas” when using Define an authoritative reusable metric view with the approved expression. The scenario instead tests “An audit requires a documented retention policy for a catalog table”; the relevant result is Owners can verify both documented retention and actual cleanup jobs.

The alternative Use `databricks bundle deploy` with explicit target and credentials has a distinct purpose—A CI workflow must deploy resource definitions across an approved target workspace. It would not establish the required outcome (Developers need one reproducible definition for jobs and pipelines across environments); the necessary verification concerns Bundle deployment resolves to consistent resources in each target workspace.

The alternative Define per-target variables in the bundle configuration has a distinct purpose—A release needs environment-specific source paths without editing source code. It would not establish the required outcome (Developers need one reproducible definition for jobs and pipelines across environments); the necessary verification concerns Bundle deployment resolves to consistent resources in each target workspace.

This action solves a different issue: “A production change must be validated before resources are created or updated.” Its normal signal is The validation detects missing or malformed bundle configuration before deployment. Here the requested behavior is “Developers need one reproducible definition for jobs and pipelines across environments,” so that signal is not sufficient.

No secret value appears in source YAML or deployment logs would demonstrate the separate action Reference a secret scope or governed connection in the task configuration, which addresses “A secret used in production must not be stored in the bundle YAML.” It does not verify the requirement “Developers need one reproducible definition for jobs and pipelines across environments” following Define resources and targets in a Declarative Automation Bundle.

The validation detects missing or malformed bundle configuration before deployment pertains to a different control (Run `databricks bundle validate -t prod` under the deployment identity) and a different need (A production change must be validated before resources are created or updated). It cannot establish that Define resources and targets in a Declarative Automation Bundle met the stated goal.

Deployed resources appear only in the intended workspace pertains to a different control (Use `databricks bundle deploy` with explicit target and credentials) and a different need (A CI workflow must deploy resource definitions across an approved target workspace). It cannot establish that Define resources and targets in a Declarative Automation Bundle met the stated goal.

The alternative Add an approval step in the CI/CD pipeline before running the production deploy has a distinct purpose—A deployment requires a human approval boundary for regulated production. It would not establish the required outcome (A production change must be validated before resources are created or updated); the necessary verification concerns The validation detects missing or malformed bundle configuration before deployment.

This action solves a different issue: “Different branches have conflicting deployment names in a shared dev workspace.” Its normal signal is Two developers can deploy their bundles without overwriting each other's resources. Here the requested behavior is “A production change must be validated before resources are created or updated,” so that signal is not sufficient.

This action solves a different issue: “A production notebook change must be peer-reviewed before deployment.” Its normal signal is Only approved merged commits enter the deployment branch. Here the requested behavior is “A production change must be validated before resources are created or updated,” so that signal is not sufficient.

This signal belongs to the adjacent case “A production notebook change must be peer-reviewed before deployment,” whose remedy is Use a Git-backed branch and pull-request review workflow. The observed result for the present requirement is The validation detects missing or malformed bundle configuration before deployment.

The pipeline blocks deployment after an intentionally failing test would demonstrate the separate action Make bundle deployment depend on automated test and bundle-validation stages, which addresses “A new bundle deployment should not reach production unless tests and validation pass.” It does not verify the requirement “A production change must be validated before resources are created or updated” following Run `databricks bundle validate -t prod` under the deployment identity.

This signal belongs to the adjacent case “Developers need one reproducible definition for jobs and pipelines across environments,” whose remedy is Define resources and targets in a Declarative Automation Bundle. The observed result for the present requirement is The validation detects missing or malformed bundle configuration before deployment.

Gate production deployment on CI test outcomes belongs to the separate case “A candidate release should be rejected if an integration test fails,” where An intentionally failing test prevents the deploy action would be expected. That differs from the condition in this stem: “A CI job should deploy a reviewed release into production with the correct target.”

Use development targets with isolate-friendly naming or resource prefixes is appropriate when “Different branches have conflicting deployment names in a shared dev workspace”; it would be verified by Two developers can deploy their bundles without overwriting each other's resources. It does not directly satisfy “A CI job should deploy a reviewed release into production with the correct target,” whose relevant acceptance evidence is The target workspace shows the deployed resource definitions.

Configure an authorized service principal for production deployment belongs to the separate case “A production target must not use the developer's personal credentials,” where Deployment records show the service principal as the acting identity would be expected. That differs from the condition in this stem: “A CI job should deploy a reviewed release into production with the correct target.”

Development deploy succeeds while unauthorized production deploy is denied would demonstrate the separate action Separate target-specific deployment permissions by identity, which addresses “A new team member should deploy to development but not production.” It does not verify the requirement “A CI job should deploy a reviewed release into production with the correct target” following Run `databricks bundle deploy -t prod` under approved credentials.

Bundle deployment resolves to consistent resources in each target workspace pertains to a different control (Define resources and targets in a Declarative Automation Bundle) and a different need (Developers need one reproducible definition for jobs and pipelines across environments). It cannot establish that Run `databricks bundle deploy -t prod` under approved credentials met the stated goal.

This signal belongs to the adjacent case “A CI workflow must deploy resource definitions across an approved target workspace,” whose remedy is Use `databricks bundle deploy` with explicit target and credentials. The observed result for the present requirement is The target workspace shows the deployed resource definitions.

This action solves a different issue: “Developers need one reproducible definition for jobs and pipelines across environments.” Its normal signal is Bundle deployment resolves to consistent resources in each target workspace. Here the requested behavior is “An unwanted test resource from a bundle should be deleted under controlled lifecycle,” so that signal is not sufficient.

This action solves a different issue: “A new bundle deployment should not reach production unless tests and validation pass.” Its normal signal is The pipeline blocks deployment after an intentionally failing test. Here the requested behavior is “An unwanted test resource from a bundle should be deleted under controlled lifecycle,” so that signal is not sufficient.

Use `databricks bundle deploy` with explicit target and credentials belongs to the separate case “A CI workflow must deploy resource definitions across an approved target workspace,” where Deployed resources appear only in the intended workspace would be expected. That differs from the condition in this stem: “An unwanted test resource from a bundle should be deleted under controlled lifecycle.”

This signal belongs to the adjacent case “A new bundle deployment should not reach production unless tests and validation pass,” whose remedy is Make bundle deployment depend on automated test and bundle-validation stages. The observed result for the present requirement is Only resources tracked by the selected deployment are removed as expected.

Two developers can deploy their bundles without overwriting each other's resources pertains to a different control (Use development targets with isolate-friendly naming or resource prefixes) and a different need (Different branches have conflicting deployment names in a shared dev workspace). It cannot establish that Use supported bundle destroy/remove workflow for the correct target after review met the stated goal.

No secret value appears in source YAML or deployment logs pertains to a different control (Reference a secret scope or governed connection in the task configuration) and a different need (A secret used in production must not be stored in the bundle YAML). It cannot establish that Use supported bundle destroy/remove workflow for the correct target after review met the stated goal.

Run `databricks bundle deploy -t prod` under approved credentials belongs to the separate case “A CI job should deploy a reviewed release into production with the correct target,” where The target workspace shows the deployed resource definitions would be expected. That differs from the condition in this stem: “Different branches have conflicting deployment names in a shared dev workspace.”

The alternative Separate target-specific deployment permissions by identity has a distinct purpose—A new team member should deploy to development but not production. It would not establish the required outcome (Different branches have conflicting deployment names in a shared dev workspace); the necessary verification concerns Two developers can deploy their bundles without overwriting each other's resources.

Use `databricks bundle deploy` with explicit target and credentials belongs to the separate case “A CI workflow must deploy resource definitions across an approved target workspace,” where Deployed resources appear only in the intended workspace would be expected. That differs from the condition in this stem: “Different branches have conflicting deployment names in a shared dev workspace.”

That observation is a valid indicator for “A workspace notebook differs from the deployed Git commit unexpectedly” when using Synchronize or redeploy from the canonical reviewed Git revision. The scenario instead tests “Different branches have conflicting deployment names in a shared dev workspace”; the relevant result is Two developers can deploy their bundles without overwriting each other's resources.

This signal belongs to the adjacent case “A CI workflow must deploy resource definitions across an approved target workspace,” whose remedy is Use `databricks bundle deploy` with explicit target and credentials. The observed result for the present requirement is Two developers can deploy their bundles without overwriting each other's resources.

Only resources tracked by the selected deployment are removed as expected would demonstrate the separate action Use supported bundle destroy/remove workflow for the correct target after review, which addresses “An unwanted test resource from a bundle should be deleted under controlled lifecycle.” It does not verify the requirement “Different branches have conflicting deployment names in a shared dev workspace” following Use development targets with isolate-friendly naming or resource prefixes.