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Databricks Databricks-Certified-Professional-Data-Engineer Exam Questions
Certified Data Engineer Professional (Page 2 )

Updated On: 23-Apr-2026
Page 2 of 44
PDF with 339 Questions

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?

  1. date = spark.conf.get("date")
  2. input_dict = input()
    date= input_dict["date"]
  3. import sys
    date = sys.argv[1]
  4. date = dbutils.notebooks.getParam("date")
  5. dbutils.widgets.text("date", "null")
    date = dbutils.widgets.get("date")

Answer(s): E



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.

  1. "Can Manage" privileges on the required cluster
  2. Workspace Admin privileges, cluster creation allowed, "Can Attach To" privileges on the required cluster
  3. Cluster creation allowed, "Can Attach To" privileges on the required cluster
  4. "Can Restart" privileges on the required cluster
  5. Cluster creation allowed, "Can Restart" privileges on the required cluster

Answer(s): D



When scheduling Structured Streaming jobs for production, which configuration automatically recovers from query failures and keeps costs low?

  1. Cluster: New Job Cluster;
    Retries: Unlimited;
    Maximum Concurrent Runs: Unlimited
  2. Cluster: New Job Cluster;
    Retries: None;
    Maximum Concurrent Runs: 1
  3. Cluster: Existing All-Purpose Cluster;
    Retries: Unlimited;
    Maximum Concurrent Runs: 1
  4. Cluster: New Job Cluster;
    Retries: Unlimited;
    Maximum Concurrent Runs: 1
  5. Cluster: Existing All-Purpose Cluster;
    Retries: None;
    Maximum Concurrent Runs: 1

Answer(s): D



The data engineering team has configured a Databricks SQL query and alert to monitor the values in a Delta Lake table. The recent_sensor_recordings table contains an identifying sensor_id alongside the timestamp and temperature for the most recent 5 minutes of recordings.

The below query is used to create the alert:



The query is set to refresh each minute and always completes in less than 10 seconds. The alert is set to trigger when mean (temperature) > 120. Notifications are triggered to be sent at most every 1 minute.

If this alert raises notifications for 3 consecutive minutes and then stops, which statement must be true?

  1. The total average temperature across all sensors exceeded 120 on three consecutive executions of the query
  2. The recent_sensor_recordings table was unresponsive for three consecutive runs of the query
  3. The source query failed to update properly for three consecutive minutes and then restarted
  4. The maximum temperature recording for at least one sensor exceeded 120 on three consecutive executions of the query
  5. The average temperature recordings for at least one sensor exceeded 120 on three consecutive executions of the query

Answer(s): E



A junior developer complains that the code in their notebook isn't producing the correct results in the development environment. A shared screenshot reveals that while they're using a notebook versioned with Databricks Repos, they're using a personal branch that contains old logic. The desired branch named dev- 2.3.9 is not available from the branch selection dropdown.

Which approach will allow this developer to review the current logic for this notebook?

  1. Use Repos to make a pull request use the Databricks REST API to update the current branch to dev- 2.3.9
  2. Use Repos to pull changes from the remote Git repository and select the dev-2.3.9 branch.
  3. Use Repos to checkout the dev-2.3.9 branch and auto-resolve conflicts with the current branch
  4. Merge all changes back to the main branch in the remote Git repository and clone the repo again
  5. Use Repos to merge the current branch and the dev-2.3.9 branch, then make a pull request to sync with the remote repository

Answer(s): B



The security team is exploring whether or not the Databricks secrets module can be leveraged for connecting to an external database.

After testing the code with all Python variables being defined with strings, they upload the password to the secrets module and configure the correct permissions for the currently active user. They then modify their code to the following (leaving all other variables unchanged).



Which statement describes what will happen when the above code is executed?

  1. The connection to the external table will fail; the string "REDACTED" will be printed.
  2. An interactive input box will appear in the notebook; if the right password is provided, the connection will succeed and the encoded password will be saved to DBFS.
  3. An interactive input box will appear in the notebook; if the right password is provided, the connection will succeed and the password will be printed in plain text.
  4. The connection to the external table will succeed; the string value of password will be printed in plain text.
  5. The connection to the external table will succeed; the string "REDACTED" will be printed.

Answer(s): E



The data science team has created and logged a production model using MLflow. The following code correctly imports and applies the production model to output the predictions as a new DataFrame named preds with the schema "customer_id LONG, predictions DOUBLE, date DATE".



The data science team would like predictions saved to a Delta Lake table with the ability to compare all predictions across time. Churn predictions will be made at most once per day.

Which code block accomplishes this task while minimizing potential compute costs?

  1. preds.write.mode("append").saveAsTable("churn_preds")
  2. preds.write.format("delta").save("/preds/churn_preds")
    C.
    D.
    E.

Answer(s): A



An upstream source writes Parquet data as hourly batches to directories named with the current date. A nightly batch job runs the following code to ingest all data from the previous day as indicated by the date variable:



Assume that the fields customer_id and order_id serve as a composite key to uniquely identify each order.

If the upstream system is known to occasionally produce duplicate entries for a single order hours apart, which statement is correct?

  1. Each write to the orders table will only contain unique records, and only those records without duplicates in the target table will be written.
  2. Each write to the orders table will only contain unique records, but newly written records may have duplicates already present in the target table.
  3. Each write to the orders table will only contain unique records; if existing records with the same key are present in the target table, these records will be overwritten.
  4. Each write to the orders table will only contain unique records; if existing records with the same key are present in the target table, the operation will fail.
  5. Each write to the orders table will run deduplication over the union of new and existing records, ensuring no duplicate records are present.

Answer(s): B



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Databricks-Certified-Professional-Data-Engineer Exam Discussions & Posts

What the Databricks-Certified-Professional-Data-Engineer Exam Tests and How to Pass It

The Databricks-Certified-Professional-Data-Engineer certification is designed for experienced data engineers who are responsible for building, deploying, and maintaining complex data pipelines within the Databricks Data Intelligence Platform. This certification validates that a professional possesses the advanced technical skills required to manage the entire data lifecycle, from initial ingestion and acquisition to sophisticated transformation, modelling, and final delivery. Organizations that hire for this role are typically looking for individuals who can not only write efficient code but also architect scalable solutions that adhere to best practices in data governance, security, and cost management. Because this is a professional-level credential, it serves as a benchmark for senior-level proficiency, demonstrating that the candidate can handle the nuances of production-grade data environments where performance, reliability, and compliance are critical business requirements.

Achieving this Databricks certification signifies that a candidate has moved beyond basic platform familiarity and has developed a deep, practical understanding of how to optimize data workflows for high-volume, high-velocity data processing. It is highly regarded in the industry because it requires candidates to demonstrate applied knowledge in real-world scenarios, such as troubleshooting failed jobs, managing complex dependencies, and ensuring that data is both secure and accessible to the right stakeholders. Professionals who hold this certification are often tasked with leading data engineering teams, setting standards for code quality, and making architectural decisions that directly impact the efficiency and cost-effectiveness of an organization's data infrastructure. By validating these competencies, the exam ensures that certified engineers are capable of delivering robust, production-ready data solutions that drive meaningful business insights.

What the Databricks-Certified-Professional-Data-Engineer Exam Covers

The scope of the Databricks-Certified-Professional-Data-Engineer exam is comprehensive, covering the entire spectrum of tasks a data engineer performs daily. Candidates must demonstrate proficiency in developing code for data processing using Python and SQL, which serves as the foundation for building scalable pipelines. The exam tests your ability to handle data ingestion and acquisition from diverse sources, ensuring that data is brought into the lakehouse environment efficiently and reliably. Furthermore, you will be evaluated on your skills in data transformation, cleansing, and quality, which are essential for maintaining the integrity of the data being processed. The curriculum also encompasses data sharing and federation, allowing you to understand how to securely expose data to downstream consumers. Finally, the exam requires a solid grasp of monitoring and alerting, cost and performance optimisation, ensuring data security and compliance, data governance, and the complexities of debugging and deploying code, alongside advanced data modelling techniques. Our practice questions are designed to mirror these domains, providing you with the necessary exposure to the types of technical challenges you will encounter on the actual exam.

Among these topics, the areas of cost and performance optimisation, combined with debugging and deploying, are often considered the most technically demanding aspects of the certification exam. These domains require candidates to move past simple syntax knowledge and instead demonstrate an ability to analyze execution plans, identify bottlenecks in Spark jobs, and implement strategies to reduce compute costs without sacrificing performance. You must understand how to effectively manage cluster configurations, utilize appropriate file formats, and implement partitioning strategies that minimize data shuffling. Additionally, the ability to diagnose and resolve deployment failures in a CI/CD context is a critical skill that separates experienced engineers from those who are just starting. Candidates need to show they can interpret error logs, manage library dependencies, and ensure that production pipelines are resilient to failures, which is why our practice questions focus heavily on these scenario-based problem-solving tasks.

Are These Real Databricks-Certified-Professional-Data-Engineer Exam Questions?

It is important to clarify that our platform does not provide leaked, confidential, or unauthorized exam content. Instead, our practice questions are sourced and verified by the community, consisting of IT professionals and recent test-takers who have sat for the actual exam and contributed their knowledge to help others succeed. Because these questions are community-verified, they reflect the style, difficulty, and technical focus of the real exam questions you will face on test day. If you've been searching for Databricks-Certified-Professional-Data-Engineer exam dumps or braindump files, our community-verified practice questions offer something more valuable — each question is verified and explained by IT professionals who recently passed the exam. This approach ensures that you are studying high-quality, relevant material that aligns with the current exam objectives rather than relying on outdated or inaccurate information.

The community verification process is what makes our platform a reliable resource for your exam preparation. When a question is added to our database, it undergoes a rigorous review where users discuss the answer choices, flag potentially incorrect information, and provide context based on their own recent exam experiences. This collaborative environment allows you to see the reasoning behind each answer, which is far more effective for long-term retention than simply memorizing a list of answers. By engaging with these discussions, you gain insights into the "why" behind the correct answer, which is essential for passing a professional-level certification exam that tests your ability to apply knowledge in complex, real-world scenarios.

How to Prepare for the Databricks-Certified-Professional-Data-Engineer Exam

Effective exam preparation requires a balanced approach that combines theoretical study with significant hands-on practice in a Databricks environment. You should prioritize building and deploying pipelines in a sandbox or development workspace, as this practical experience is the only way to truly understand how the platform behaves under different configurations. Rely heavily on official Databricks documentation to clarify concepts, but use our practice questions to test your application of that knowledge in a structured way. Every practice question includes a free AI Tutor explanation that breaks down the reasoning behind the correct answer — so you understand the concept, not just the answer. This AI Tutor is an invaluable tool for identifying gaps in your knowledge, allowing you to focus your study time on the areas where you need the most improvement.

A common mistake candidates make when preparing for this Databricks certification is relying too heavily on rote memorization of facts or definitions. The exam is heavily scenario-based, meaning you will be presented with a business problem or a technical constraint and asked to choose the best architectural or coding solution. To avoid this pitfall, you must practice analyzing these scenarios critically, considering factors like cost, performance, and maintainability before selecting an answer. Time management is another critical factor; during your exam preparation, simulate the testing environment by timing yourself as you work through sets of questions. This will help you build the stamina and speed required to complete the exam within the allotted time, ensuring you do not rush through complex questions that require careful thought.

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On the day of your Databricks-Certified-Professional-Data-Engineer exam, you should be prepared for a rigorous assessment that tests your ability to apply technical knowledge in a professional setting. The exam is typically administered in a proctored environment, either at a physical testing center or through an online proctoring service, ensuring the integrity of the certification process. You can expect a series of multiple-choice and scenario-based questions that require you to select the most efficient, secure, or cost-effective solution from a list of options. The questions are designed to be challenging, often presenting multiple technically viable solutions where only one is the "best" choice based on Databricks best practices. Familiarize yourself with the exam interface and the types of questions beforehand so that you can focus entirely on the technical content during the test.

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These practice questions are intended for data engineers who have significant experience working with the Databricks platform and are looking to validate their expertise through the official certification exam. Typically, candidates should have at least a year or more of hands-on experience in a production environment, as the exam assumes a level of familiarity with common data engineering challenges and Databricks-specific features. Whether you are looking to advance your career, demonstrate your value to your current employer, or simply master the platform, this certification is a powerful tool for professional growth. By using our platform for your exam preparation, you are setting yourself up to approach the certification exam with confidence, knowing that you have practiced with high-quality, community-verified material.

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Updated on: 27 April, 2026

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