A data engineer is implementing Unity Catalog governance for a multi-team environment. Data scientists need interactive clusters for basic data exploration tasks, while automated ETL jobs require dedicated processing. How should the data engineer configure cluster isolation policies to enforce least privilege and ensure Unity Catalog compliance?

• A. Create compute policies with STANDARD access mode for interactive workloads and DEDICATED access mode for automated jobs.
• B. Allow all users to create any cluster type and rely on manual configuration to enable Unity Catalog access modes.
• C. Configure all clusters with NO ISOLATION_SHARED access mode since Unity Catalog works with any cluster configuration.
• D. Use only DEDICATED access mode for both interactive workloads and automated jobs to maximize security isolation.

A data engineer is configuring a Databricks Asset Bundle to deploy a job with granular permissions.
The requirements are:
- Grant the data-engineers group CAN_MANAGE access to the job.
- Ensure the auditors' group can view the job but not modify/run it.
- Avoid granting unintended permissions to other users/groups.
How should the data engineer deploy the job while meeting the requirements?

• A. resources:
  jobs:
  my-job:
  name: data-pipeline
  tasks: [...]
  job_clusters: [...]
  permissions:
  - group_name: data-engineers
  level: CAN_MANAGE
  - group_name: auditors
  level: CAN_VIEW
  - group_name: admin-team
  level: IS_OWNER
• B. permissions:
  - group_name: data-engineers
  level: CAN_MANAGE
  - group_name: auditors
  level: CAN_VIEW
  resources:
  jobs:
  my-job:
  name: data-pipeline
  tasks: [...]
  job_clusters: [...]
• C. resources:
  jobs:
  my-job:
  name: data-pipeline
  tasks: [...]
  job_clusters: [...]
  permissions:
  - group_name: data-engineers
  level: CAN_MANAGE
  - group_name: auditors
  level: CAN_VIEW
• D. resources:
  jobs:
  my-job:
  name: data-pipeline
  tasks: [...]
  job: [...]
  permissions:
  - group_name: data-engineers
  level: CAN_MANAGE
  permissions:
  - group_name: auditors
  level: CAN_VIEW

The data architect has mandated that all tables in the Lakehouse should be configured as external Delta Lake tables.
Which approach will ensure that this requirement is met?

• A. Whenever a table is being created, make sure that the location keyword is used.
• B. Whenever a database is being created, make sure that the location keyword is used
• C. When configuring an external data warehouse for all table storage. leverage Databricks for all ELT.
• D. When tables are created, make sure that the external keyword is used in the create table statement.
• E. When the workspace is being configured, make sure that external cloud object storage has been mounted.

A faulty IoT sensor in a factory reports a temperature of -500, causing the LDP pipeline to fail the expectation, which only allows values between -100 and 200 degrees Celsius. The data engineer would like to further analyze the faulty data to better understand the reason behind this. How should the data engineer resolve the faulty data while ensuring data quality standards are maintained?

• A. Fix the pipeline code and implement a quarantine logic to isolate the faulty data before re-running the pipeline.
• B. Ignore the error and simply re-run the pipeline, as Databricks will automatically skip the problematic record on the next run.
• C. Remove all expectations form the pipeline to prevent any future failures, regardless of data quality.
• D. Change the expectation action from fail to warn so that invalid records are included in the output and the pipeline does not fail.