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Databricks Databricks-Certified-Data-Engineer-Professional

Databricks-Certified-Data-Engineer-Professional

Exam Code: Databricks-Certified-Data-Engineer-Professional

Exam Name: Databricks Certified Data Engineer Professional Exam

Updated: May 30, 2026

Q&A Number: 250 Q&As

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


2. A junior data engineer seeks to leverage Delta Lake's Change Data Feed functionality to create a Type 1 table representing all of the values that have ever been valid for all rows in a bronze table created with the property delta.enableChangeDataFeed = true. They plan to execute the following code as a daily job:

Which statement describes the execution and results of running the above query multiple times?

A) Each time the job is executed, only those records that have been inserted or updated since the last execution will be appended to the target table giving the desired result.
B) Each time the job is executed, the target table will be overwritten using the entire history of inserted or updated records, giving the desired result.
C) Each time the job is executed, newly updated records will be merged into the target table, overwriting previous values with the same primary keys.
D) Each time the job is executed, the entire available history of inserted or updated records will be appended to the target table, resulting in many duplicate entries.
E) Each time the job is executed, the differences between the original and current versions are calculated; this may result in duplicate entries for some records.


3. Two of the most common data locations on Databricks are the DBFS root storage and external object storage mounted with dbutils.fs.mount().
Which of the following statements is correct?

A) DBFS is a file system protocol that allows users to interact with files stored in object storage using syntax and guarantees similar to Unix file systems.
B) The DBFS root stores files in ephemeral block volumes attached to the driver, while mounted directories will always persist saved data to external storage between sessions.
C) Neither the DBFS root nor mounted storage can be accessed when using %sh in a Databricks notebook.
D) The DBFS root is the most secure location to store data, because mounted storage volumes must have full public read and write permissions.
E) By default, both the DBFS root and mounted data sources are only accessible to workspace administrators.


4. Which of the following is true of Delta Lake and the Lakehouse?

A) Delta Lake automatically collects statistics on the first 32 columns of each table which are leveraged in data skipping based on query filters.
B) Z-order can only be applied to numeric values stored in Delta Lake tables
C) Views in the Lakehouse maintain a valid cache of the most recent versions of source tables at all times.
D) Because Parquet compresses data row by row. strings will only be compressed when a character is repeated multiple times.
E) Primary and foreign key constraints can be leveraged to ensure duplicate values are never entered into a dimension table.


5. A data engineer is working in an interactive notebook with many transformations before outputting the result from display(df.collect() ). The notebook includes wide transformations and a cross join.
The data engineer is getting the following error: "The spark driver has stopped unexpectedly and is restarting. Your notebook will be automatically reattached." Which action should the data engineer take?

A) Rewrite their code to avoid putting memory pressure on the driver node.
B) Run the notebook on a single node cluster to keep driver from falling.
C) Look at the compute metrics UI to see if the executors have higher than 90% memory utilization.
D) Check into the Spark UI to see how many jobs are assigned to each stage as they are employing fewer executors.


Solutions:

Question # 1
Answer: B
Question # 2
Answer: D
Question # 3
Answer: A
Question # 4
Answer: A
Question # 5
Answer: A

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