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


"Databricks Certified Data Engineer Professional", also known as Certified-Data-Engineer-Professional exam, is a Databricks Certification. With the complete collection of questions and answers, PrepAwayTest has assembled to take you through 250 Q&As to your Certified-Data-Engineer-Professional Exam preparation. In the Certified-Data-Engineer-Professional exam resources, you will cover every field and category in Databricks Certification Certification helping to ready you for your successful Databricks Certification.

  • Exam Code: Certified-Data-Engineer-Professional
  • Exam Name: Databricks Certified Data Engineer Professional
  • Total Questions: 250

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Databricks Certified-Data-Engineer-Professional Exam Overview:

Certification Vendor:Databricks
Exam Name:Databricks Certified Data Engineer Professional
Exam Number:Certified-Data-Engineer-Professional
Available Languages:English
Exam Duration:120 minutes
Real Exam Qty:59 scored multiple-choice questions
Related Certifications:Databricks Certified Data Engineer Associate
Exam Format:Test center proctored, Online proctored, Multiple-choice questions
Exam Price:USD 200 plus applicable taxes
Certificate Validity Period:2 years
Recommended Training:Databricks Academy
Advanced Data Engineering with Databricks
Exam Registration:Databricks Certified Data Engineer Professional Certification
Sample Questions: DOWNLOAD DEMO
Exam Way:Online proctored or test center proctored
Pre Condition:No prerequisite is required. Related course attendance and one year of hands-on experience in data engineering tasks covered by the exam are highly recommended.
Official Syllabus URL:https://www.databricks.com/sites/default/files/2025-11/databricks-certified-data-engineer-professional-exam-guide-november-30-2025.pdf

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

SectionObjectives
Developing Code for Data Processing using Python and SQL- Building and Testing an ETL Pipeline with Lakeflow Declarative Pipelines, SQL, and Apache Spark
  • 1. Explain the advantages and disadvantages of streaming tables compared to materialized views
    • 2. Create pipeline components using control flow operators such as if/else and foreach
      • 3. Create and automate ETL workloads using Jobs through the UI, APIs, or CLI
        • 4. Choose appropriate configurations for environments, dependencies, high-memory notebook tasks, and retry behavior
          • 5. Build and manage reliable, production-ready batch and streaming data pipelines using Lakeflow Declarative Pipelines and Auto Loader
            • 6. Develop unit and integration tests using assertDataFrameEqual, assertSchemaEqual, DataFrame.transform, testing frameworks, and debugging tools
              • 7. Compare Spark Structured Streaming and Lakeflow Declarative Pipelines to determine the optimal approach for scalable ETL pipelines
                • 8. Use APPLY CHANGES APIs to simplify CDC in Lakeflow Declarative Pipelines
                  - Using Python and Tools for Development
                  • 1. Design and implement a scalable Python project structure optimized for Databricks Asset Bundles, enabling modular development, deployment automation, and CI/CD integration
                    • 2. Develop User-Defined Functions using Pandas/Python UDF
                      • 3. Manage and troubleshoot external third-party library installations and dependencies, including PyPI packages, local wheels, and source archives
                        Data Modeling- Design and optimize data models
                        • 1. Design and implement scalable data models using Delta Lake to manage large datasets
                          • 2. Design dimensional models for analytical workloads with efficient querying and aggregation
                            • 3. Simplify data layout decisions and optimize query performance using liquid clustering
                              • 4. Identify the benefits of liquid clustering over partitioning and Z-Ordering
                                Data Ingestion & Acquisition- Design and implement data ingestion pipelines
                                • 1. Ingest formats including Delta Lake, Parquet, ORC, AVRO, JSON, CSV, XML, text, and binary data from sources such as message buses and cloud storage
                                  • 2. Create an append-only data pipeline capable of handling both batch and streaming data using Delta
                                    Data Transformation, Cleansing, and Quality- Transform and validate data
                                    • 1. Write efficient Spark SQL and PySpark code for advanced transformations including window functions, joins, and aggregations
                                      • 2. Develop a quarantining process for bad data with Lakeflow Declarative Pipelines or Auto Loader in classic jobs
                                        Monitoring and Alerting- Monitoring
                                        • 1. Use Query Profile and Spark UI to monitor workloads
                                          • 2. Use Databricks REST APIs and Databricks CLI to monitor jobs and pipelines
                                            • 3. Use system tables for observability of resource utilization, cost, auditing, and workloads
                                              • 4. Use Lakeflow Declarative Pipelines event logs to monitor pipelines
                                                - Alerting
                                                • 1. Use SQL Alerts to monitor data quality
                                                  • 2. Use the Workflows UI and Jobs API to configure notifications for job status and performance issues
                                                    Debugging and Deploying- Deploying CI/CD
                                                    • 1. Configure and integrate Git-based CI/CD workflows using Databricks Git folders for notebook and code deployment
                                                      • 2. Build and deploy Databricks resources using Databricks Asset Bundles
                                                        - Debugging and Troubleshooting
                                                        • 1. Identify diagnostic information using Spark UI, cluster logs, system tables, and query profiles to troubleshoot errors
                                                          • 2. Analyze errors and remediate failed job runs using job repairs and parameter overrides
                                                            • 3. Use Lakeflow Declarative Pipelines event logs and Spark UI to debug Lakeflow Declarative Pipelines and Spark pipelines
                                                              Data Sharing and Federation- Share and federate data
                                                              • 1. Configure Lakehouse Federation with appropriate governance across supported source systems
                                                                • 2. Demonstrate secure Delta Sharing between Databricks deployments using Databricks-to-Databricks sharing or with external platforms using the open sharing protocol
                                                                  • 3. Use Delta Sharing to share live data from the Lakehouse with any computing platform
                                                                    Cost & Performance Optimization- Optimize cost and performance
                                                                    • 1. Use query profiling to identify bottlenecks such as inefficient joins and data shuffling
                                                                      • 2. Understand Delta optimization techniques such as deletion vectors and liquid clustering
                                                                        • 3. Understand Databricks query optimization techniques for large datasets, including data skipping and file pruning
                                                                          • 4. Apply Change Data Feed to address streaming table limitations and improve latency
                                                                            • 5. Understand how and why Unity Catalog managed tables reduce operational overhead and maintenance burden
                                                                              Ensuring Data Security and Compliance- Ensuring Compliance
                                                                              • 1. Develop data purging solutions that comply with data retention policies
                                                                                • 2. Implement compliant batch and streaming pipelines that detect and mask PII
                                                                                  - Applying Data Security Mechanisms
                                                                                  • 1. Use ACLs to secure workspace objects and enforce the principle of least privilege
                                                                                    • 2. Use row filters and column masks to protect sensitive table data
                                                                                      • 3. Apply anonymization and pseudonymization methods including hashing, tokenization, suppression, and generalization
                                                                                        Data Governance- Govern enterprise data
                                                                                        • 1. Create and add descriptions and metadata to enterprise data to improve discoverability
                                                                                          • 2. Demonstrate understanding of the Unity Catalog permission inheritance model

                                                                                            Before You Book: Databricks Certified Data Engineer Professional Questions and Answers

                                                                                            At its core, the Certified-Data-Engineer-Professional exam is Databricks's gatekeeper for the Databricks Certified Data Engineer Professional certification — a Professional-level credential. It examines how well you apply the Databricks Certified Data Engineer Professional objectives to realistic scenarios, which is why hands-on candidates tend to outperform pure memorizers. The credential also links into a wider path covering Databricks Certified Data Engineer Associate, so it works as both a standalone achievement and a stepping stone. In a market where certificates increasingly signal ability, passing Certified-Data-Engineer-Professional is a concrete, verifiable upgrade to your professional profile.

                                                                                            The Databricks Certified Data Engineer Professional exam draws its questions from 10 domains, starting with Data Transformation, Cleansing, and Quality, Data Modeling, and Debugging and Deploying. Smart candidates read weightings as a resource map: heavy domains get the deepest practice, light domains get efficient review. You'll find the complete domain list in the exam topics section above — let it drive your schedule.

                                                                                            The Databricks Certified Data Engineer Professional exam fits 59 scored multiple-choice questions questions into 120 minutes. Convert that into a pace before exam day: total minutes divided by question count gives you a per-item budget, and the discipline to flag-and-return when something overruns it. Practicing under a live timer — the PrepAwayTest test engines run one on every session — turns that discipline into reflex, so the clock works for you instead of against you.

                                                                                            Officially, the Databricks Certified Data Engineer Professional exam uses these formats: Multiple-choice questions, Online proctored, Test center proctored. Formats shape strategy — long scenario stems reward reading the final question sentence first, while multi-select items punish partial knowledge. The fix is exposure: PrepAwayTest's 250 practice questions rehearse you in every listed format until the mechanics are second nature and only the content demands your attention.

                                                                                            The credential earned through the Databricks Certified Data Engineer Professional exam remains valid for 2 years. Note the expiry date the day you pass and start researching recertification options well in advance — planned renewals are routine, last-minute ones are stressful. Because recertification rules belong to Databricks and change over time, verify the current policy on the official certification page as your renewal window approaches.

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                                                                                            Databricks Certified Data Engineer Professional Sample Questions:

                                                                                            Which statement describes integration testing?

                                                                                            • A. Requires manual intervention
                                                                                            • B. Validates interactions between subsystems of your application
                                                                                            • C. Validates an application use case
                                                                                            • D. Validates behavior of individual elements of your application
                                                                                            • E. Requires an automated testing framework
                                                                                            Reveal Solution  Discussion  0

                                                                                            Correct Answer: B  🗳️

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                                                                                            The data governance team is reviewing code used for deleting records for compliance with GDPR. They note the following logic is used to delete records from the Delta Lake table named users.

                                                                                            Assuming that user_id is a unique identifying key and that delete_requests contains all users that have requested deletion, which statement describes whether successfully executing the above logic guarantees that the records to be deleted are no longer accessible and why?

                                                                                            • A. No; files containing deleted records may still be accessible with time travel until a vacuum command is used to remove invalidated data files.
                                                                                            • B. No; the Delta cache may return records from previous versions of the table until the cluster is restarted.
                                                                                            • C. No; the Delta Lake delete command only provides ACID guarantees when combined with the merge into command.
                                                                                            • D. Yes; the Delta cache immediately updates to reflect the latest data files recorded to disk.
                                                                                            • E. Yes; Delta Lake ACID guarantees provide assurance that the delete command succeeded fully and permanently purged these records.
                                                                                            Reveal Solution  Discussion  0

                                                                                            Correct Answer: A  🗳️

                                                                                            Explanation: Only visible for PrepAwayTest members. You can sign-up / login (it's free).

                                                                                            Which statement describes the default execution mode for Databricks Auto Loader?

                                                                                            • A. New files are identified by listing the input directory; the target table is materialized by directory querying all valid files in the source directory.
                                                                                            • B. Cloud vendor-specific queue storage and notification services are configured to track newly arriving files; the target table is materialized by directly querying all valid files in the source directory.
                                                                                            • C. Webhook trigger Databricks job to run anytime new data arrives in a source directory; new data automatically merged into target tables using rules inferred from the data.
                                                                                            • D. Cloud vendor-specific queue storage and notification services are configured to track newly arriving files; new files are incrementally and impotently into the target Delta Lake table.
                                                                                            • E. New files are identified by listing the input directory; new files are incrementally and idempotently loaded into the target Delta Lake table.
                                                                                            Reveal Solution  Discussion  0

                                                                                            Correct Answer: E  🗳️

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                                                                                            A data architect has designed a system in which two Structured Streaming jobs will concurrently write to a single bronze Delta table. Each job is subscribing to a different topic from an Apache Kafka source, but they will write data with the same schema. To keep the directory structure simple, a data engineer has decided to nest a checkpoint directory to be shared by both streams.
                                                                                            The proposed directory structure is displayed below:

                                                                                            Which statement describes whether this checkpoint directory structure is valid for the given scenario and why?

                                                                                            • A. No; Delta Lake manages streaming checkpoints in the transaction log.
                                                                                            • B. No; each of the streams needs to have its own checkpoint directory.
                                                                                            • C. Yes; Delta Lake supports infinite concurrent writers.
                                                                                            • D. Yes; both of the streams can share a single checkpoint directory.
                                                                                            • E. No; only one stream can write to a Delta Lake table.
                                                                                            Reveal Solution  Discussion  0

                                                                                            Correct Answer: B  🗳️

                                                                                            Explanation: Only visible for PrepAwayTest members. You can sign-up / login (it's free).

                                                                                            A Delta Lake table representing metadata about content from user has the following schema:
                                                                                            user_id LONG, post_text STRING, post_id STRING, longitude FLOAT, latitude FLOAT, post_time TIMESTAMP, date DATE Based on the above schema, which column is a good candidate for partitioning the Delta Table?

                                                                                            • A. Date
                                                                                            • B. Post_time
                                                                                            • C. User_id
                                                                                            • D. Post_id
                                                                                            • E. latitude
                                                                                            Reveal Solution  Discussion  0

                                                                                            Correct Answer: A  🗳️

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