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

Databricks Certified Data Engineer Professional Sample Questions:

Question 1

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

Question 2

A data architect is implementing Delta Sharing as part of their data governance strategy to enable secure data collaboration with external partners and internal business units. The architect must establish a permission framework that allows designated data stewards to create shares for their respective domains while maintaining security boundaries and audit compliance. Which specific permissions and roles must be assigned to enable users to create, configure, and manage Delta Shares while maintaining proper security governance and access controls?

A. Users need to be metastore admins or have CREATE SHARE privilege for the metastore
B. Only workspace admins can create and manage shares
C. Users need the MANAGE SHARES permission on the workspace
D. Any user with USE_CATALOG privilege can create shares

Question 3

A junior member of the data engineering team is exploring the language interoperability of Databricks notebooks. The intended outcome of the below code is to register a view of all sales that occurred in countries on the continent of Africa that appear in the geo_lookup table.
Before executing the code, running SHOW TABLES on the current database indicates the database contains only two tables: geo_lookup and sales.

Which statement correctly describes the outcome of executing these command cells in order in an interactive notebook?

A. Cmd 1 will succeed and Cmd 2 will fail, countries at will be a Python variable containing a list of strings.
B. Both commands will fail. No new variables, tables, or views will be created.
C. Both commands will succeed. Executing show tables will show that countries at and sales at have been registered as views.
D. Cmd 1 will succeed and Cmd 2 will fail, countries at will be a Python variable representing a PySpark DataFrame.
E. Cmd 1 will succeed. Cmd 2 will search all accessible databases for a table or view named countries af: if this entity exists, Cmd 2 will succeed.

Question 4

A company stores account transactions in a Delta Lake table. The company needs to apply frequent account-level correlations (e.g., UPDATE statements) but wants to avoid rewriting entire Parquet files for each change to reduce file churn and improve write performance. Which Delta Lake feature should they enable?

A. Enable automatic file compaction on writes
B. Enable change data feed on the Delta table
C. Partition the Delta table by account_id
D. Enable deletion vectors on the Delta table

Question 5

Which statement describes Delta Lake Auto Compaction?

A. Optimized writes use logical partitions instead of directory partitions; because partition boundaries are only represented in metadata, fewer small files are written.
B. Before a Jobs cluster terminates, optimize is executed on all tables modified during the most recent job.
C. An asynchronous job runs after the write completes to detect if files could be further compacted; if yes, an optimize job is executed toward a default of 128 MB.
D. Data is queued in a messaging bus instead of committing data directly to memory; all data is committed from the messaging bus in one batch once the job is complete.
E. An asynchronous job runs after the write completes to detect if files could be further compacted; if yes, an optimize job is executed toward a default of 1 GB.

Solutions: