Try each one before opening the answer. Every option is explained, with the Databricks documentation page that proves it.
Question 1 · choose 1
A shared integration target of a bundle uses mode: development so that schedules stay paused. Two CI runners sometimes deploy it at the same moment, and the deployment state ends up inconsistent. The team wants to keep development mode. What should they change?
ASet bundle.deployment.lock.enabled to true for the target
BRun the deploys with --force
CSet jobs_max_concurrent_runs to 1 in the target's presets
DSwitch the target to mode: production
Show the answer and why
ASet bundle.deployment.lock.enabled to true for the target
Correct
Development mode turns off the deployment lock; enabling it again stops concurrent deployments from conflicting.
BRun the deploys with --force
Incorrect
--force overrides validations; it does not serialize deployments.
CSet jobs_max_concurrent_runs to 1 in the target's presets
Incorrect
This limits job runs, not bundle deployments.
DSwitch the target to mode: production
Incorrect
That gives up the development-mode behavior the team wants to keep.
Among other behaviors, development mode disables the deployment lock for faster iteration, because conflicts are unlikely for one developer. For a shared target with concurrent deployers, set bundle.deployment.lock.enabled to true.
A team deploys a bundle's prod target with mode: production. Which TWO behaviors does production mode apply? (Choose TWO.)
AIt validates that the deployed pipelines are not in development mode
BIt rejects overriding the bundle's cluster definitions, such as with --compute-id
CIt pauses all schedules and triggers of the deployed jobs
DIt prefixes resource names with the deploying user's name
EIt enables concurrent runs on every deployed job
Show the answer and why
AIt validates that the deployed pipelines are not in development mode
Correct
Production mode validates that related pipelines are marked development: false.
BIt rejects overriding the bundle's cluster definitions, such as with --compute-id
Correct
Unlike development mode, production mode does not allow overriding existing cluster definitions.
CIt pauses all schedules and triggers of the deployed jobs
Incorrect
Pausing schedules is a development mode behavior.
DIt prefixes resource names with the deploying user's name
Incorrect
The name prefix is applied in development mode.
EIt enables concurrent runs on every deployed job
Incorrect
Allowing concurrent runs for faster iteration is a development mode behavior.
Deployment modes apply groups of settings. Development mode prefixes names, pauses schedules and allows cluster overrides, while production mode validates pipeline settings and the Git branch and keeps the defined clusters.
A bundle deploys ten jobs and two pipelines. The data_ops group must have CAN_VIEW on all of them. Where is this set most simply?
AIn each resource's tags, as data_ops: CAN_VIEW
BIn the bundle's variables, one per resource
CIn a top-level permissions mapping of the bundle
DIn the workspace mapping of the prod target
Show the answer and why
AIn each resource's tags, as data_ops: CAN_VIEW
Incorrect
Tags do not grant permissions.
BIn the bundle's variables, one per resource
Incorrect
Variables hold values; they do not set permissions by themselves.
CIn a top-level permissions mapping of the bundle
Correct
Top-level permissions apply to all resources defined in the bundle.
DIn the workspace mapping of the prod target
Incorrect
workspace sets paths and the host, not resource permissions.
The permissions mapping, at the top level or per target, applies the given levels for users, groups or service principals to the resources the bundle defines.
A prod target with mode: production is deployed by a person rather than a service principal, and validation fails. What does production mode check in this case?
AThat every job in the target has a schedule or trigger
BThat the deploying user is a workspace admin in the prod workspace
CThat all notebooks in the bundle are in IPYNB format
DThat run_as and permissions are set and paths are not user paths
Show the answer and why
AThat every job in the target has a schedule or trigger
Incorrect
Schedules are not validated.
BThat the deploying user is a workspace admin in the prod workspace
Incorrect
The checks concern identities and paths, not the admin role.
CThat all notebooks in the bundle are in IPYNB format
Incorrect
Notebook format is not checked.
DThat run_as and permissions are set and paths are not user paths
Correct
Without service principals, production mode validates these mappings and paths.
Databricks recommends service principals for production deployments. If none are used, production mode validates that artifact, file, root and state paths are not set to a specific user and that run_as and permissions are specified.
After deploying a bundle to the staging target, an engineer wants a list of the deployed jobs and pipelines with their workspace URLs. Which command shows this?
Adatabricks bundle summary -t staging
Bdatabricks bundle validate -t staging
Cdatabricks bundle init
Ddatabricks bundle destroy -t staging
Show the answer and why
Adatabricks bundle summary -t staging
Correct
bundle summary reviews everything deployed for the target.
Bdatabricks bundle validate -t staging
Incorrect
validate checks the configuration.
Cdatabricks bundle init
Incorrect
init creates a new bundle from a template.
Ddatabricks bundle destroy -t staging
Incorrect
destroy removes deployed resources.
After deployment, databricks bundle summary lists what the bundle deployed to a target.
A bundle defines resources: jobs: nightly_etl with name 'Nightly ETL'. In dev mode it deploys as '[dev ada] Nightly ETL'. Which command runs it?
Adatabricks bundle run -t dev "[dev ada] Nightly ETL"
Bdatabricks bundle run -t dev nightly_etl
Cdatabricks bundle run -t dev "Nightly ETL"
Ddatabricks jobs run-now nightly_etl
Show the answer and why
Adatabricks bundle run -t dev "[dev ada] Nightly ETL"
Incorrect
The deployed display name is not the identifier.
Bdatabricks bundle run -t dev nightly_etl
Correct
bundle run takes the key of the resource in the bundle configuration.
Cdatabricks bundle run -t dev "Nightly ETL"
Incorrect
The name field is not the key that bundle run uses.
Ddatabricks jobs run-now nightly_etl
Incorrect
jobs run-now needs the numeric job ID.
Resources are identified in a bundle by their keys under resources. databricks bundle run <key> starts the deployed job or pipeline for the chosen target.