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Databricks Certified Data Engineer Professional Sample Questions:

1. A data engineer is brining an existing production Databricks job under asset bundle management and wants to ensure that:
- The job's current configuration is captured as YAML, and all
referenced files are included in their bundle project.
- Future changes to the bundle's YAML will update the existing job in-
place (not create a new job)
How should the data engineer successfully move the production job under asset bundle management?

A) Manually create the YAML configuration for the job in your bundle project, ensuring all settings match the existing job. Then, run Databricks bundle deploy the bundle, which will update the existing job in your workspace.
B) Run databricks bundle generate job --existing-job-id to generate the YAML and download referenced files. Then, run Databricks bundle deployment, bind to link the bundle's job resource to the existing job in Databricks.
C) Export the job definition as JSON, convert it to YAML, and place it in your bundle. Then, run Databricks bundle deploy to update the existing job.
D) Run Databricks bundle generate job --existing-job-id to generate the YAML and download referenced files. Then, run Databricks bundle deploy to deploy the bundle, which will always update the existing job automatically.

2. A data company uses Databricks Unity Catalog and has multiple enterprise data sources, including PostgreSQL, Snowflake, and SQL Server. The central data platform team wants to configure Lakehouse Federation so analysts can query external tables directly in Databricks using Databricks SQL, without duplicating data. Which steps are necessary to configure Lakehouse Federation in a secure and governed manner?

A) Use Partner Connect to create linked datasets, and apply table ACLs at the source system to govern access through Databricks.
B) Mirror the external datasets into Delta Lake using Auto Loader, and govern them using Data Lineage and System Tables.
C) Configure connections and foreign catalog in Unity Catalog, then grant access to foreign catalogs, schemas, and tables using Unity Catalog permissions.
D) Create external locations and storage credentials to connect to each database, then register foreign tables in Unity Catalog.

3. A data engineer is configuring Delta Sharing for a Databricks-to-Databricks scenario to optimize read performance. The recipient needs to perform time travel queries and streaming reads on shared sales data. Which configuration will provide the optimal performance while enabling these capabilities?

A) Share tables WITHOUT HISTORY and enable partitioning for better query performance.
B) Share tables WITH HISTORY, ensure tables don't have partitioning enabled, and enable CDF before sharing.
C) Share the entire schema WITHOUT HISTORY and rely on recipient-side caching for performance.
D) Use the open sharing protocol instead of Databricks-to-Databricks sharing for better performance.

4. A data engineer is building a Lakeflow Declarative Pipelines pipeline to process healthcare claims data. A metadata JSON file defines data quality rules for multiple tables, including:
{
"claims": [
{"name": "valid_patient_id", "constraint": "patient_id IS NOT NULL"},
{"name": "non_negative_amount", "constraint": "claim_amount >= 0"}
]
}
The pipeline must dynamically apply these rules to the claims table without hardcoding the rules.
How should the data engineer achieve this?

A) Load the JSON metadata, loop through its entries, and apply expectations using dlt.expect_all.
B) Reference each expectation with @dlt.expect decorators in the table declaration.
C) Use a SQL CONSTRAINT block referencing the JSON file path.
D) Invoke an external API to validate records against the metadata rules.

5. 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 deployment, builds, context, identity, and environments; set dev as default environment and override paths under builds.
C) Use roots, modules, profiles, actor, and targets; where profiles contain workspace and artifacts defaults and actor sets run identity.
D) 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.

Solutions: