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Databricks Databricks-Certified-Professional-Data-Engineer Practice Test Questions Answers

Exam Code: Databricks-Certified-Professional-Data-Engineer (Updated 195 Q&As with Explanation)
 Exam Name: Databricks Certified Data Engineer Professional Exam
 Last Update: 14-Dec-2025
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Questions Include:

  • Single Choice: 192 Q&A's
  • Multiple Choice: 3 Q&A's

    • Databricks-Certified-Professional-Data-Engineer Overview

    Databricks Databricks-Certified-Professional-Data-Engineer Exam Overview

    Overview Details
    Exam Name Databricks Certified Professional Data Engineer
    Certification Earned Databricks Certified Professional Data Engineer
    Target Audience Data engineers with 1+ years of experience performing advanced data engineering tasks using Databricks
    Pre-requisites Databricks Certified Data Engineer Associate certification recommended
    Number of Questions 60 multiple-choice and performance-based questions
    Exam Length 120 minutes
    Passing Score 70%
    Exam Cost $300 USD
    Delivery Format Proctored online or at a Pearson VUE testing center
    Topics Covered

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    Databricks Databricks-Certified-Professional-Data-Engineer Exam Topics Breakdown

    Section Focus Weighting Approx. Questions
    Advanced Data Engineering Building and optimizing complex data pipelines, data modeling, advanced transformations 30% 18
    Databricks Platform and Tools Deep understanding of Databricks features and tools 25% 15
    Security and Reliability Securing and automating data pipelines, monitoring and troubleshooting 20% 12
    Automation and Optimization Automating tasks, optimizing resources, custom development 15% 9

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    Databricks Databricks-Certified-Professional-Data-Engineer Exam Dumps FAQs

    The Databricks-Certified-Professional-Data-Engineer exam is a certification exam that assesses an individual ability to use Databricks to perform advanced data engineering tasks. This includes an understanding of the Databricks platform and developer tools like Apache Spark, Delta Lake, MLflow, and the Databricks CLI and REST API. It also assesses the ability to build optimized and cleaned ETL pipelines. Additionally, the ability to model data into a lakehouse using knowledge of general data modeling concepts will be assessed. Finally, being able to ensure that data pipelines are secure, reliable, monitored and tested before deployment will also be included in this exam.

    The difficulty level of the Databricks-Certified-Professional-Data-Engineer exam is considered to be moderate to high. The Databricks exam assesses an individual ability to use Databricks to perform advanced data engineering tasks, and requires a good understanding of the Databricks platform and developer tools like Apache Spark, Delta Lake, MLflow, and the Databricks CLI and REST API.

    To book a slot for the Databricks-Certified-Professional-Data-Engineer exam, you can visit the official Databricks website and register for the exam. The exam is delivered online and is proctored, which means that you will need to have a webcam and microphone to take the exam.

    Solution2Pass offers a comprehensive set of practice questions and answers that can help you prepare for the Databricks-Certified-Professional-Data-Engineer exam. These practice questions are designed to simulate the actual exam and help you identify areas where you need to improve. Additionally, Solution2Pass offers PDF questions and answers, as well as a testing engine that can help you assess your readiness for the exam.

    Databricks-Certified-Professional-Data-Engineer recommends having at least one year of experience working as a data engineer or a related role, with hands-on experience using Databricks and Apache Spark. Familiarity with cloud platforms and data warehousing concepts is also beneficial.

    The Databricks-Certified-Professional-Data-Engineer exam consists of 60 multiple-choice questions.

    The Databricks-Certified-Professional-Data-Engineer certification is valid for 2 years from the date of issue.

    The recommended experience for taking the Databricks-Certified-Professional-Data-Engineer exam is 1+ years of hands-on experience performing the data engineering tasks outlined in the exam guide.

    Databricks-Certified-Professional-Data-Engineer Questions and Answers

    Question # 1

    A data architect has heard about lake's built-in versioning and time travel capabilities. For auditing purposes they have a requirement to maintain a full of all valid street addresses as they appear in the customers table.

    The architect is interested in implementing a Type 1 table, overwriting existing records with new values and relying on Delta Lake time travel to support long-term auditing. A data engineer on the project feels that a Type 2 table will provide better performance and scalability.

    Which piece of information is critical to this decision?

    A.

    Delta Lake time travel does not scale well in cost or latency to provide a long-term versioning solution.

    B.

    Delta Lake time travel cannot be used to query previous versions of these tables because Type 1 changes modify data files in place.

    C.

    Shallow clones can be combined with Type 1 tables to accelerate historic queries for long-term versioning.

    D.

    Data corruption can occur if a query fails in a partially completed state because Type 2 tables requires

    Setting multiple fields in a single update.

    Question # 2

    A junior data engineer is working to implement logic for a Lakehouse table named silver_device_recordings. The source data contains 100 unique fields in a highly nested JSON structure.

    The silver_device_recordings table will be used downstream for highly selective joins on a number of fields, and will also be leveraged by the machine learning team to filter on a handful of relevant fields, in total, 15 fields have been identified that will often be used for filter and join logic.

    The data engineer is trying to determine the best approach for dealing with these nested fields before declaring the table schema.

    Which of the following accurately presents information about Delta Lake and Databricks that may Impact their decision-making process?

    A.

    Because Delta Lake uses Parquet for data storage, Dremel encoding information for nesting can be directly referenced by the Delta transaction log.

    B.

    Tungsten encoding used by Databricks is optimized for storing string data: newly-added native support for querying JSON strings means that string types are always most efficient.

    C.

    Schema inference and evolution on Databricks ensure that inferred types will always accurately match the data types used by downstream systems.

    D.

    By default Delta Lake collects statistics on the first 32 columns in a table; these statistics are leveraged for data skipping when executing selective queries.

    Question # 3

    The DevOps team has configured a production workload as a collection of notebooks scheduled to run daily using the Jobs Ul. A new data engineering hire is onboarding to the team and has requested access to one of these notebooks to review the production logic.

    What are the maximum notebook permissions that can be granted to the user without allowing accidental changes to production code or data?

    A.

    Can manage

    B.

    Can edit

    C.

    Can run

    D.

    Can Read

    Question # 4

    Which statement describes Delta Lake Auto Compaction?

    A.

    An asynchronous job runs after the write completes to detect if files could be further compacted; if yes, an optimize job is executed toward a default of 1 GB.

    B.

    Before a Jobs cluster terminates, optimize is executed on all tables modified during the most recent job.

    C.

    Optimized writes use logical partitions instead of directory partitions; because partition boundaries are only represented in metadata, fewer small files are written.

    D.

    Data is queued in a messaging bus instead of committing data directly to memory; all data is committed from the messaging bus in one batch once the job is complete.

    E.

    An asynchronous job runs after the write completes to detect if files could be further compacted; if yes, an optimize job is executed toward a default of 128 MB.

    Question # 5

    Which statement describes integration testing?

    A.

    Validates interactions between subsystems of your application

    B.

    Requires an automated testing framework

    C.

    Requires manual intervention

    D.

    Validates an application use case

    E.

    Validates behavior of individual elements of your application

    Free Databricks-Certified-Professional-Data-Engineer Databricks Exam Questions 6 to 25
    Free Databricks-Certified-Professional-Data-Engineer Databricks Exam Questions 26 to 45

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