Databricks Databricks-Certified-Professional-Data-Engineer Practice Test - 100% Exam Passing Guarantee (2026)

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

Section	Objectives
Topic 1: Production Pipelines and Orchestration	- Build and manage workflows using Databricks Jobs - Automate ETL pipelines and scheduling - Pipeline reliability and fault tolerance
Topic 2: Data Ingestion and Transformation	- Ingest data using Apache Spark and Databricks - Handle batch and streaming data pipelines - Transform and clean datasets using Spark SQL and DataFrame APIs
Topic 3: Data Modeling and Storage	- Design scalable data lakehouse architectures - Schema evolution and data partitioning strategies - Delta Lake table design and optimization
Topic 4: Security, Governance, Monitoring, and Optimization	- Monitor and optimize Spark workloads - Implement Unity Catalog governance and access control - Cost optimization and performance tuning

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Databricks Certified Professional Data Engineer Exam Sample Questions (Q11-Q16):

NEW QUESTION # 11
When writing streaming data, Spark's structured stream supports the below write modes

A. Delta, Complete, Continuous
B. Complete, Incremental, Update
C. Append, overwrite, Continuous
D. Append, Complete, Update
E. Append, Delta, Complete

Answer: D

Explanation:
Explanation
The answer is Append, Complete, Update
*Append mode (default) - This is the default mode, where only the new rows added to the Result Table since the last trigger will be outputted to the sink. This is supported for only those queries where rows added to the Result Table is never going to change. Hence, this mode guarantees that each row will be output only once (assuming fault-tolerant sink). For example, queries with only select, where, map, flatMap, filter, join, etc. will support Append mode.
*Complete mode - The whole Result Table will be outputted to the sink after every trigger. This is supported for aggregation queries.
*Update mode - (Available since Spark 2.1.1) Only the rows in the Result Table that were updated since the last trigger will be outputted to the sink. More information to be added in future releases.

NEW QUESTION # 12
An upstream system has been configured to pass the date for a given batch of data to the Databricks Jobs API as a parameter. The notebook to be scheduled will use this parameter to load data with the following code:
df = spark.read.format("parquet").load(f"/mnt/source/(date)")
Which code block should be used to create the date Python variable used in the above code block?

A. dbutils.widgets.text("date", "null")
date = dbutils.widgets.get("date")
B. date = spark.conf.get("date")
C. input_dict = input()
date= input_dict["date"]
D. import sys
date = sys.argv[1]
E. date = dbutils.notebooks.getParam("date")

Answer: E

Explanation:
Explanation
This is the correct way to get a parameter passed to a notebook by the Databricks Jobs API. The dbutils.notebooks.getParam method returns the value of a parameter passed to a notebook as a string. If no parameter with that name is passed, it returns None by default. You can also specify a default value as a second argument. Verified References: Databricks Certified Data Engineer Professional, under "Databricks Tooling" section; Databricks Documentation, under "Pass parameters to a notebook" section.

NEW QUESTION # 13
Which statement describes Delta Lake optimized writes?

A. Before a job cluster terminates, OPTIMIZE is executed on all tables modified during the most recent job.
B. 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.
C. Optimized writes logical partitions instead of directory partitions partition boundaries are only represented in metadata fewer small files are written.
D. 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.

Answer: B

Explanation:
Delta Lake optimized writes involve a shuffle operation before writing out data to the Delta table. The shuffle operation groups data by partition keys, which can lead to a reduction in the number of output files and potentially larger files, instead of multiple smaller files. This approach can significantly reduce the total number of files in the table, improve read performance by reducing the metadata overhead, and optimize the table storage layout, especially for workloads with many small files.
References:
* Databricks documentation on Delta Lake performance tuning:
https://docs.databricks.com/delta/optimizations/auto-optimize.html

NEW QUESTION # 14
You were asked to create a notebook that can take department as a parameter and process the data accordingly, which is the following statements result in storing the notebook parameter into a py-thon variable

A. department = notebook.widget.get("department")
B. SET department = dbutils.widget.get("department")
C. ASSIGN department == dbutils.widget.get("department")
D. department = dbutils.widget.get("department")
E. department = notebook.param.get("department")

Answer: D

Explanation:
Explanation
The answer is department = dbutils.widget.get("department")
Refer to additional documentation here
https://docs.databricks.com/notebooks/widgets.html

NEW QUESTION # 15
Which of the following SQL statement can be used to query a table by eliminating duplicate rows from the query results?

A. SELECT DISTINCT_ROWS (*) FROM table_name
B. SELECT DISTINCT * FROM table_name HAVING COUNT(*) > 1
C. SELECT * FROM table_name GROUP BY * HAVING COUNT(*) < 1
D. SELECT * FROM table_name GROUP BY * HAVING COUNT(*) > 1
E. SELECT DISTINCT * FROM table_name

Answer: E

Explanation:
Explanation
The answer is SELECT DISTINCT * FROM table_name

NEW QUESTION # 16
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