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

Databricks Certified Data Engineer Professional Sample Questions:

1. A company wants to implement Lakehouse Federation across multiple data sources but is concerned about data consistency and ensuring that all teams access the same authoritative version of their data. Which statement is applicable for Lakehouse Federations to maintain data consistency?

A) Federation provides read-only access that reflects the current state of source systems.
B) A separate data synchronization service must be deployed.
C) Federation implements change data capture (CDC) from all sources.
D) Federation creates local copies that must be manually refreshed.

2. A data engineer created a daily batch ingestion pipeline using a cluster with the latest DBR version to store banking transaction data, and persisted it in a MANAGED DELTA table called prod.gold.all_banking_transactions_daily. The data engineer is constantly receiving complaints from business users who query this table ad hoc through a SQL Serverless Warehouse about poor query performance. Upon analysis, the data engineer identified that these users frequently use high- cardinality columns as filters. The engineer now seeks to implement a data layout optimization technique that is incremental, easy to maintain, and can evolve over time. Which command should the data engineer implement?

A) Alter the table to use Z-ORDER and implement a periodic OPTIMIZE command.
B) Alter the table to use Hive-Style Partitions and implement a periodic OPTIMIZE command.
C) Alter the table to use Liquid Clustering and implement a periodic OPTIMIZE command.
D) Alter the table to use Hive-Style Partitions + Z-ORDER and implement a periodic OPTIMIZE command.

3. A departing platform owner currently holds ownership of multiple catalogs and controls storage credentials and external locations. A data engineer has been asked to ensure continuity: transfer catalog ownership to the platform team group, delegate ongoing privilege management, and retain the ability to receive and share data via Delta Sharing. Which role must be in place to perform these actions across the metastore?

A) Workspace Admin, because workspace admins can transfer ownership of any Unity Catalog object.
B) Account Admin, because account admins can only create metastores but cannot change ownership of catalogs.
C) Catalog Owner, because catalog owners can transfer any object in any catalog in the metastore.
D) Metastore Admin, because metastore admins can transfer ownership and manage privileges across all metastore objects, including shares and recipients.

4. A data engineer wants to refactor the following DLT code, which includes multiple table definitions with very similar code.

In an attempt to programmatically create these tables using a parameterized table definition, the data engineer writes the following code.

The pipeline runs an update with this refactored code, but generates a different DAG showing incorrect configuration values for these tables.
How can the data engineer fix this?

A) Convert the list of configuration values to a dictionary of table settings, using table names as keys.
B) Convert the list of configuration values to a dictionary of table settings, using different input the for loop.
C) Wrap the loop inside another table definition, using generalized names and properties to replace with those from the inner table
D) Load the configuration values for these tables from a separate file, located at a path provided by a pipeline parameter.

5. A data engineer is analyzing a large, partitioned retail dataset in Databricks, where each row represents a sale made by a salesperson. The dataset contains millions of records with the following schema:
sales_df: [salesperson_id: string, region: string, sale_amount: double, sale_date: date] The data engineer needs to generate a DataFrame that ranks salespeople within each region based on their total cumulative sales, with the highest seller ranked as 1. If multiple salespeople have the same total sales, they should share the same rank.
The data engineer wants to implement this logic using a PySpark window function and the dense_rank () function.
Which code snippet will perform this ranking?

A)

B)

C)

D)

Solutions: