Databricks Certified-Data-Engineer-Professional Actual Tests : Databricks Certified Data Engineer Professional

A team of data engineer are adding tables to a DLT pipeline that contain repetitive expectations for many of the same data quality checks.
One member of the team suggests reusing these data quality rules across all tables defined for this pipeline.
What approach would allow them to do this?

• A. Maintain data quality rules in a Delta table outside of this pipeline's target schema, providing the schema name as a pipeline parameter.
• B. Add data quality constraints to tables in this pipeline using an external job with access to pipeline configuration files.
• C. Maintain data quality rules in a separate Databricks notebook that each DLT notebook of file.
• D. Use global Python variables to make expectations visible across DLT notebooks included in the same pipeline.

A data engineer manages a Unity Catalog table customer_data in schema finance that includes sensitive fields like ssn and credit_score. Intern Group should only see masked values, while Analyst Group should only access rows for their assigned region. The data engineer needs to restrict access based on user role and region without duplicating data. How should the data engineer enforce this security policy?

• A. Create dynamic views for each user role and manage access with ACLs.
• B. Create views using current_user() and is_account_group_member() functions, and apply masking logic inside the SQL SELECT clause for each sensitive column.
• C. Use Unity Catalog's row filters based on the region and column masks based on user roles.
• D. Use Unity Catalog's row filters based on the user roles and column masks based on the region.

Which statement describes Delta Lake optimized writes?

• A. A shuffle occurs prior to writing to try to group data together resulting in fewer files instead of each executor writing multiple files based on directory partitions.
• B. An asynchronous job runs after the write completes to detect if files could be further compacted; yes, an OPTIMIZE job is executed toward a default of 1 GB.
• C. Before a job cluster terminates, OPTIMIZE is executed on all tables modified during the most recent job.
• D. Optimized writes logical partitions instead of directory partitions partition boundaries are only represented in metadata fewer small files are written.

The data engineering team is migrating an enterprise system with thousands of tables and views into the Lakehouse. They plan to implement the target architecture using a series of bronze, silver, and gold tables. Bronze tables will almost exclusively be used by production data engineering workloads, while silver tables will be used to support both data engineering and machine learning workloads. Gold tables will largely serve business intelligence and reporting purposes. While personal identifying information (PII) exists in all tiers of data, pseudonymization and anonymization rules are in place for all data at the silver and gold levels.
The organization is interested in reducing security concerns while maximizing the ability to collaborate across diverse teams.
Which statement exemplifies best practices for implementing this system?

• A. Isolating tables in separate databases based on data quality tiers allows for easy permissions management through database ACLs and allows physical separation of default storage locations for managed tables.
• B. Because databases on Databricks are merely a logical construct, choices around database organization do not impact security or discoverability in the Lakehouse.
• C. Because all tables must live in the same storage containers used for the database they're created in, organizations should be prepared to create between dozens and thousands of databases depending on their data isolation requirements.
• D. Storinq all production tables in a single database provides a unified view of all data assets available throughout the Lakehouse, simplifying discoverability by granting all users view privileges on this database.
• E. Working in the default Databricks database provides the greatest security when working with managed tables, as these will be created in the DBFS root.

The marketing team is looking to share data in an aggregate table with the sales organization, but the field names used by the teams do not match, and a number of marketing specific fields have not been approval for the sales org.
Which of the following solutions addresses the situation while emphasizing simplicity?

• A. Instruct the marketing team to download results as a CSV and email them to the sales organization.
• B. Create a new table with the required schema and use Delta Lake's DEEP CLONE functionality to sync up changes committed to one table to the corresponding table.
• C. Create a view on the marketing table selecting only these fields approved for the sales team alias the names of any fields that should be standardized to the sales naming conventions.
• D. Use a CTAS statement to create a derivative table from the marketing table configure a production jon to propagation changes.
• E. Add a parallel table write to the current production pipeline, updating a new sales table that varies as required from marketing table.