Databricks Certified-Data-Engineer-Professional : Databricks Certified Data Engineer Professional

A data engineering team is configuring access controls in Databricks Unity Catalog. They grant the SELECT privilege on the sales catalog to the analyst_group, expecting that members of this group will automatically have SELECT access to all current and future schemas, tables, and views within the catalog. What describes the privilege inheritance behavior in Unity Catalog?

• A. Privileges granted at the schema level override any catalog-level privileges and prevent access unless explicitly revoked.
• B. Privileges in Unity Catalog do not cascade; SELECT must be explicitly granted on each schema and table, even if granted at the catalog level.
• C. Granting SELECT on a catalog automatically applies SELECT to all current and future schemas, tables, and views within that catalog.
• D. Granting SELECT at the catalog level applies to existing schemas and tables but not to those created in the future.

A data engineer is implementing a job to download multiple PDF files from a third-party provided REST API endpoint by specifying different report types. The REST API is time-consuming and encounters intermittent errors, so the engineer wants to track each download activity to know when it fails and to retry partially, while providing scalable throughput. The engineer needs to download ten report types, and the list can be changed over time. How should the data engineer achieve this?

• A. Define ten Notebook tasks to clearly track which report download failed.
• B. Use a foreach task with a list of report types as its inputs.
• C. Define a list variable within a Notebook to loop through the report types to download them, and print the download results. Execute it as a Notebook tasks.
• D. Use a Delta Lake table to track each report download status as 10 rows, and use it as a source table to execute the download function as a Pandas UDF.

A data engineer is creating a daily reporting job. There are two reporting notebooks--one for weekdays and one for weekends. An "if/else condition" task is configured as
{{job.start_time.is_weekday}} == true to route the job to either the weekday or weekend notebook tasks. The same job would be used across multiple time zones. Which action should a senior data engineer take upon reviewing the job to merge or reject the pull request?

• A. Reject, as the {{job.start_time.is_weekday}} is for the UTC timezone.
• B. Merge, as the job configuration looks good.
• C. Reject, as they should use {{job.trigger_time.is_weekday}} instead.
• D. Reject, as the {{job.start_time.is_weekday}} is not a valid value reference.

A platform team is creating a standardized template for Databricks Asset Bundles to support CI/CD. The template must specify defaults for artifacts, workspace root paths, and a run identity, while allowing a "dev" target to be the default and override specific paths. How should the team use databricks.yml to satisfy these requirements?

• A. Use project, packages, environment, identity, and stages; set dev as default stage and override workspace under environment.
• B. Use roots, modules, profiles, actor, and targets; where profiles contain workspace and artifacts defaults and actor sets run identity.
• C. Use bundle, artifacts, workspace, run_as, and targets at the top level; set one target with default:true and override workspace paths or artifacts under that target.
• D. Use deployment, builds, context, identity, and environments; set dev as default environment and override paths under builds.

Incorporating unit tests into a PySpark application requires upfront attention to the design of your jobs, or a potentially significant refactoring of existing code.
Which statement describes a main benefit that offset this additional effort?

• A. Yields faster deployment and execution times
• B. Validates a complete use case of your application
• C. Ensures that all steps interact correctly to achieve the desired end result
• D. Troubleshooting is easier since all steps are isolated and tested individually
• E. Improves the quality of your data