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

  • Exam Code: Certified-Data-Engineer-Professional
  • Exam Name: Databricks Certified Data Engineer Professional
  • Updated: Oct 26, 2026
  • Q & A: 250 Questions and Answers

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About Databricks Certified-Data-Engineer-Professional Exam Questions

Reading notes is one thing; sitting a timed exam is another. The BraindumpsPrep test engines recreate the rhythm of the Certified-Data-Engineer-Professional exam with timed practice sessions, so nothing on exam day feels unfamiliar. Combined with 250 practice questions, it is the closest rehearsal available in 2026.

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

SectionObjectives
Topic 1: Data Governance- Govern enterprise data
  • 1. Demonstrate understanding of the Unity Catalog permission inheritance model
    • 2. Create and add descriptions and metadata to enterprise data to improve discoverability
      Topic 2: Data Modeling- Design and optimize data models
      • 1. Design dimensional models for analytical workloads with efficient querying and aggregation
        • 2. Design and implement scalable data models using Delta Lake to manage large datasets
          • 3. Simplify data layout decisions and optimize query performance using liquid clustering
            • 4. Identify the benefits of liquid clustering over partitioning and Z-Ordering
              Topic 3: Ensuring Data Security and Compliance- Applying Data Security Mechanisms
              • 1. Apply anonymization and pseudonymization methods including hashing, tokenization, suppression, and generalization
                • 2. Use ACLs to secure workspace objects and enforce the principle of least privilege
                  • 3. Use row filters and column masks to protect sensitive table data
                    - 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
                        Topic 4: 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
                            Topic 5: Cost & Performance Optimization- Optimize cost and performance
                            • 1. Apply Change Data Feed to address streaming table limitations and improve latency
                              • 2. Understand Delta optimization techniques such as deletion vectors and liquid clustering
                                • 3. Use query profiling to identify bottlenecks such as inefficient joins and data shuffling
                                  • 4. Understand Databricks query optimization techniques for large datasets, including data skipping and file pruning
                                    • 5. Understand how and why Unity Catalog managed tables reduce operational overhead and maintenance burden
                                      Topic 6: Debugging and Deploying- Debugging and Troubleshooting
                                      • 1. Identify diagnostic information using Spark UI, cluster logs, system tables, and query profiles to troubleshoot errors
                                        • 2. Use Lakeflow Declarative Pipelines event logs and Spark UI to debug Lakeflow Declarative Pipelines and Spark pipelines
                                          • 3. Analyze errors and remediate failed job runs using job repairs and parameter overrides
                                            - 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
                                                Topic 7: Developing Code for Data Processing using Python and SQL- Building and Testing an ETL Pipeline with Lakeflow Declarative Pipelines, SQL, and Apache Spark
                                                • 1. Compare Spark Structured Streaming and Lakeflow Declarative Pipelines to determine the optimal approach for scalable ETL pipelines
                                                  • 2. Use APPLY CHANGES APIs to simplify CDC in Lakeflow Declarative Pipelines
                                                    • 3. Build and manage reliable, production-ready batch and streaming data pipelines using Lakeflow Declarative Pipelines and Auto Loader
                                                      • 4. Create pipeline components using control flow operators such as if/else and foreach
                                                        • 5. Explain the advantages and disadvantages of streaming tables compared to materialized views
                                                          • 6. Create and automate ETL workloads using Jobs through the UI, APIs, or CLI
                                                            • 7. Develop unit and integration tests using assertDataFrameEqual, assertSchemaEqual, DataFrame.transform, testing frameworks, and debugging tools
                                                              • 8. Choose appropriate configurations for environments, dependencies, high-memory notebook tasks, and retry behavior
                                                                - Using Python and Tools for Development
                                                                • 1. Manage and troubleshoot external third-party library installations and dependencies, including PyPI packages, local wheels, and source archives
                                                                  • 2. Design and implement a scalable Python project structure optimized for Databricks Asset Bundles, enabling modular development, deployment automation, and CI/CD integration
                                                                    • 3. Develop User-Defined Functions using Pandas/Python UDF
                                                                      Topic 8: 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
                                                                          Topic 9: Data Sharing and Federation- Share and federate data
                                                                          • 1. Demonstrate secure Delta Sharing between Databricks deployments using Databricks-to-Databricks sharing or with external platforms using the open sharing protocol
                                                                            • 2. Configure Lakehouse Federation with appropriate governance across supported source systems
                                                                              • 3. Use Delta Sharing to share live data from the Lakehouse with any computing platform
                                                                                Topic 10: Monitoring and Alerting- Monitoring
                                                                                • 1. Use Databricks REST APIs and Databricks CLI to monitor jobs and pipelines
                                                                                  • 2. Use Lakeflow Declarative Pipelines event logs to monitor pipelines
                                                                                    • 3. Use system tables for observability of resource utilization, cost, auditing, and workloads
                                                                                      • 4. Use Query Profile and Spark UI to monitor workloads
                                                                                        - 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

                                                                                            Certified-Data-Engineer-Professional Exam FAQ: What Candidates Ask Most

                                                                                            The Databricks Certified Data Engineer Professional (exam code Certified-Data-Engineer-Professional) is the exam you pass to earn the Databricks Certified Data Engineer Professional certification, a credential at the Professional level. It is also connected with related credentials such as Databricks Certified Data Engineer Associate. If you are mapping out a certification path with Databricks, this exam is a milestone worth planning around.

                                                                                            The Certified-Data-Engineer-Professional exam includes 59 scored multiple-choice questions questions, and you have 120 minutes to complete them. With that many questions on the clock, pacing is part of the challenge: divide your time into rough blocks as you go, and flag the items that stall you so you can circle back at the end instead of burning minutes on a single question. A week or two before your exam date, sit at least one full timed session in the BraindumpsPrep test engine under the same time limit, so the pace feels familiar rather than frantic on the day.

                                                                                            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.

                                                                                            Eligibility rules can be adjusted by Databricks over time, so before you register, confirm the current requirements on the official exam page: view the official Certified-Data-Engineer-Professional exam information.

                                                                                            You can book the Certified-Data-Engineer-Professional exam through the official registration channels below:

                                                                                            How the exam is taken: Online proctored or test center proctored.

                                                                                            Databricks lists the following official training options for candidates preparing for this exam:

                                                                                            Once you have worked through the training, measure your readiness with the 250 practice questions from BraindumpsPrep — they show you exactly which topics still need attention before you book the exam.

                                                                                            Yes. BraindumpsPrep offers a free PDF demo of the Certified-Data-Engineer-Professional practice questions, so you can judge the quality of the material firsthand before paying anything. Every purchase also includes 365 days of free updates — if Databricks changes the exam during that year, your material changes with it. And when the free update period expires, you can extend it at a 50% discount from within your member zone.

                                                                                            Every Certified-Data-Engineer-Professional purchase at BraindumpsPrep is backed by a 100% Money Back Guarantee: if you take the corresponding exam within 60 days of your purchase and do not pass, you can apply for a full refund. The guarantee has clear conditions — it does not apply if you sit the exam within 3 days of purchase, if you downloaded the material but never actually took the exam, or to free materials and expired orders, and the candidate name must match the payer name. To file a claim, send a scan of your exam enrollment slip together with your official Score Report PDF within 2 days of the exam; claims are processed within 7 days. If you would rather not take a refund, you can exchange the product instead and receive two additional exam preparation products of equal value for free, while your original product keeps its update service. Delivery itself is instant: your material is available for download right after payment and is also sent to your email within one minute — if it has not arrived within 2 hours, contact our customer service. There is no limit on how many computers you can install it on.

                                                                                            The official Databricks Certified Data Engineer Professional outline divides the exam content into 10 main domains. The first three are:

                                                                                            • Data Governance
                                                                                            • Cost & Performance Optimization
                                                                                            • Monitoring and Alerting

                                                                                            That is only the headline view — scroll back up to the Exam Topics section on this page for the complete domain-by-domain breakdown before you plan your study schedule.

                                                                                            Databricks Certified Data Engineer Professional Sample Questions:

                                                                                            Question #1

                                                                                            A data engineering team is implementing an append-only data pipeline using Delta Lake, and wants to ensure that data is never modified or deleted once written. Which Delta Lake feature should the data engineer enable to prevent modifications to existing data?

                                                                                            • A. Delta APPEND_ONLY
                                                                                            • B. Delta VACUUM
                                                                                            • C. Delta OPTIMIZE
                                                                                            • D. Delta Time Travel
                                                                                            Reveal Solution  Discussion  0

                                                                                            Correct Answer: A  🗳️

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

                                                                                            Question #2

                                                                                            The view updates represents an incremental batch of all newly ingested data to be inserted or updated in the customers table.
                                                                                            The following logic is used to process these records.

                                                                                            Which statement describes this implementation?

                                                                                            • A. The customers table is implemented as a Type 3 table; old values are maintained as a new column alongside the current value.
                                                                                            • B. The customers table is implemented as a Type 1 table; old values are overwritten by new values and no history is maintained.
                                                                                            • C. The customers table is implemented as a Type 0 table; all writes are append only with no changes to existing values.
                                                                                            • D. The customers table is implemented as a Type 2 table; old values are maintained but marked as no longer current and new values are inserted.
                                                                                            • E. The customers table is implemented as a Type 2 table; old values are overwritten and new customers are appended.
                                                                                            Reveal Solution  Discussion  0

                                                                                            Correct Answer: D  🗳️

                                                                                            Question #3

                                                                                            A team of data engineer are adding tables to a DLT pipeline that contain repetitive expectations for many of the same data quality checks.
                                                                                            One member of the team suggests reusing these data quality rules across all tables defined for this pipeline.
                                                                                            What approach would allow them to do this?

                                                                                            • A. Use global Python variables to make expectations visible across DLT notebooks included in the same pipeline.
                                                                                            • B. Maintain data quality rules in a Delta table outside of this pipeline's target schema, providing the schema name as a pipeline parameter.
                                                                                            • C. Add data quality constraints to tables in this pipeline using an external job with access to pipeline configuration files.
                                                                                            • D. Maintain data quality rules in a separate Databricks notebook that each DLT notebook of file.
                                                                                            Reveal Solution  Discussion  0

                                                                                            Correct Answer: B  🗳️

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

                                                                                            Question #4

                                                                                            While reviewing a query's execution in the Databricks Query Profiler, a data engineer observes that the Top Operators panel shows a Sort operator with high Time Spent and Memory Peak metrics. The Spark UI also reports frequent data spilling. How should the data engineer address this issue?

                                                                                            • A. Increase the number of shuffle partitions to better distribute data.
                                                                                            • B. Convert the sort operation to a filter operation.
                                                                                            • C. Switch to a broadcast join to reduce memory usage.
                                                                                            • D. Repartition the DataFrame to a single partition before sorting.
                                                                                            Reveal Solution  Discussion  0

                                                                                            Correct Answer: A  🗳️

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

                                                                                            Question #5

                                                                                            A production cluster has 3 executor nodes and uses the same virtual machine type for the driver and executor.
                                                                                            When evaluating the Ganglia Metrics for this cluster, which indicator would signal a bottleneck caused by code executing on the driver?

                                                                                            • A. Network I/O never spikes
                                                                                            • B. Overall cluster CPU utilization is around 25%
                                                                                            • C. Total Disk Space remains constant
                                                                                            • D. The five Minute Load Average remains consistent/flat
                                                                                            • E. Bytes Received never exceeds 80 million bytes per second
                                                                                            Reveal Solution  Discussion  0

                                                                                            Correct Answer: B  🗳️

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

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