Databricks Certified-Data-Engineer-Professional : Databricks Certified Data Engineer Professional
- Exam Code: Certified-Data-Engineer-Professional
- Exam Name: Databricks Certified Data Engineer Professional
- Updated: Oct 09, 2026
- Q & A: 250 Questions and Answers
About Databricks Certified-Data-Engineer-Professional Exam
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Databricks Certified-Data-Engineer-Professional Exam Syllabus Topics:
| Section | Objectives |
|---|---|
| Topic 1: Data Sharing and Federation | - Lakehouse Federation
|
| Topic 2: Developing Code for Data Processing using Python and SQL | - Using Python and Tools for Development
|
| Topic 3: Debugging and Deploying | - Deploying CI/CD
|
| Topic 4: Ensuring Data Security and Compliance | - Compliance
|
| Topic 5: Cost & Performance Optimisation | - Cost Optimization
|
| Topic 6: Data Modelling | - Scalable Data Models
|
| Topic 7: Monitoring and Alerting | - Monitoring
|
| Topic 8: Data Governance | - Unity Catalog Permissions
|
| Topic 9: Data Transformation, Cleansing, and Quality | - Data Quality
|
| Topic 10: Data Ingestion & Acquisition | - Design and implement data ingestion pipelines
|
Databricks Certified Data Engineer Professional Sample Questions:
A data engineer wants to ingest a large collection of image files (JPEG and PNG) from cloud object storage into a Unity Catalog-managed table for analysis and visualization. Which two configurations and practices are recommended to incrementally ingest these images into the table? (Choose two.)
- A. Use Auto Loader and set cloudFiles.format to "IMAGE".
- B. Use Auto Loader and set cloudFiles.format to "BINARYFILE".
- C. Use Auto Loader and set cloudFiles.format to "TEXT".
- D. Use the pathGlobFilter option to select only image files (e.g., "*.jpg,*.png").
- E. Move files to a volume and read with SQL editor.
Correct Answer: B,D 🗳️
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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. import sys
date = sys.argv[1] - B. date = spark.conf.get("date")
- C. dbutils.widgets.text("date", "null")
date = dbutils.widgets.get("date") - D. date = dbutils.notebooks.getParam("date")
- E. input_dict = input()
date= input_dict["date"]
Correct Answer: C 🗳️
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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.
Correct Answer: D 🗳️
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The Databricks workspace administrator has configured interactive clusters for each of the data engineering groups. To control costs, clusters are set to terminate after 30 minutes of inactivity.
Each user should be able to execute workloads against their assigned clusters at any time of the day.
Assuming users have been added to a workspace but not granted any permissions, which of the following describes the minimal permissions a user would need to start and attach to an already configured cluster.
- A. Cluster creation allowed. "Can Attach To" privileges on the required cluster
- B. Cluster creation allowed. "Can Restart" privileges on the required cluster
- C. "Can Restart" privileges on the required cluster
- D. Workspace Admin privileges, cluster creation allowed. "Can Attach To" privileges on the required cluster
- E. "Can Manage" privileges on the required cluster
Correct Answer: C 🗳️
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A data architect is designing a Databricks solution to efficiently process data for different business requirements. In which scenario should a data engineer use a materialized view compared to a streaming table?
- A. Implementing a CDC (Change Data Capture) pipeline that needs to detect and respond to database changes within seconds.
- B. Processing high-volume, continuous clickstream data from a website to monitor user behavior in real-time.
- C. Ingesting data from Apache Kafka topics with sub-second processing requirements for immediate alerting.
- D. Precomputing complex aggregations and joins from multiple large tables to accelerate BI dashboard performance.
Correct Answer: D 🗳️
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