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Databricks Certified-Data-Engineer-Professional Exam Overview:

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

Databricks Certified Data Engineer Professional Sample Questions:

1. A data engineer is masking a column containing email addresses. The goal is to produce output strings of identical length for all rows, while generating different outputs for different email values.
Which SQL function should be used to achieve this?

A) sha1(email)
B) sha2(email, 0)
C) hash(email)
D) mask(email, '?')

2. Each configuration below is identical to the extent that each cluster has 400 GB total of RAM, 160 total cores and only one Executor per VM.
Given a job with at least one wide transformation, which of the following cluster configurations will result in maximum performance?

A) Total VMs: 4
100 GB per Executor
40 Cores/Executor
B) Total VMs: 1
400 GB per Executor
160 Cores / Executor
C) Total VMs: 2
200 GB per Executor
80 Cores / Executor
D) Total VMs: 8
50 GB per Executor
20 Cores / Executor

3. 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) Load the configuration values for these tables from a separate file, located at a path provided by a pipeline parameter.
C) Move the table definition into a separate function, and make calls to this function using different input parameters inside the for loop.
D) Wrap the for loop inside another table definition, using generalized names and properties to replace with those from the inner table definition.

4. Which statement describes the correct use of pyspark.sql.functions.broadcast?

A) It marks a DataFrame as small enough to store in memory on all executors, allowing a broadcast join.
B) It caches a copy of the indicated table on attached storage volumes for all active clusters within a Databricks workspace.
C) It caches a copy of the indicated table on all nodes in the cluster for use in all future queries during the cluster lifetime.
D) It marks a column as having low enough cardinality to properly map distinct values to available partitions, allowing a broadcast join.
E) It marks a column as small enough to store in memory on all executors, allowing a broadcast join.

5. A Delta Lake table was created with the below query:

Realizing that the original query had a typographical error, the below code was executed:
ALTER TABLE prod.sales_by_stor RENAME TO prod.sales_by_store
Which result will occur after running the second command?

A) The table reference in the metastore is updated and no data is changed.
B) The table name change is recorded in the Delta transaction log.
C) All related files and metadata are dropped and recreated in a single ACID transaction.
D) The table reference in the metastore is updated and all data files are moved.
E) A new Delta transaction log Is created for the renamed table.

Solutions: