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Databricks Certified-Data-Engineer-Professional Exam Syllabus Topics:

Databricks Certified Data Engineer Professional Sample Questions:

1. A data engineer is implementing liquid clustering on a Delta Lale table and needs to understand how it affects data management operations. The table will be updated frequently with new data.
The table is an external table and not managed by Unity Catalog. How does liquid clustering in Delta Lake handle new data that is inserted after the initial table creation?

A) New data remains unclustered until the next OPTIMIZE operation.
B) New data is written to a staging area and clustered during scheduled maintenance.
C) New data is rejected if it doesn't match the clustering pattern.
D) New data is automatically clustered during write operations.

2. A security team wants to enforce data protection for a customer table containing customer PII data. To comply with local policies, sales team members should only see customers from their region, while non-admin users should have email addresses masked. Which implementation approach should be used when using Unity Catalog row filters and column masks?

A) Implement row filters with SQL UDFs based on user region only since column masks cannot be combined with row filters on the same table, then apply them be recreating the table with DROP TABLE and CREATE TABLE SET ROW FILTER commands.
B) Create a view with dynamic WHERE clauses for region filtering and use string replacement functions for email masking using ALTER COLUMN SET MASK command.
C) Use table ACLs to restrict access using tags with GRANT SELECT ON table_name WITH TAG command, and rely on application-level filtering for sensitive data based on user region.
D) Create SQL UDFs for row filtering based on user region and column masking based on group membership, then apply them using ALTER TABLE SET ROW FILTER and ALTER COLUMN SET MASK commands.

3. A data engineer needs to install the PyYAML Python package within an air-gapped Databricks environment. The workspace has no direct internet access to PyPI. The engineer has downloaded the .whl file locally and wants it available automatically on all new clusters. Which approach should the data engineer use?

A) Upload the PyYAML .whl file to a Unity Catalog Volume, ensure it's allow-listed, and create a cluster-scoped init script that installs it from that path.
B) Upload the PyYAML .whl file to the user home directory and create a cluster-scoped init script to install it.
C) Set up a private PyPI repository and install via pip index URL.
D) Add the .whl file to Databricks Git Repos and assume automatic installation.

4. 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 roots, modules, profiles, actor, and targets; where profiles contain workspace and artifacts defaults and actor sets run identity.
B) Use deployment, builds, context, identity, and environments; set dev as default environment and override paths under builds.
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 project, packages, environment, identity, and stages; set dev as default stage and override workspace under environment.

5. Which approach demonstrates a modular and testable way to use DataFrame transform for ETL code in PySpark?

A)

B)

C)

D)

Solutions: