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Certified-Data-Engineer-Professional Exam Simulator
  • Exam Code: Certified-Data-Engineer-Professional
  • Exam Name: Databricks Certified Data Engineer Professional
  • Updated: Aug 30, 2026
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Databricks Certified-Data-Engineer-Professional Exam Syllabus Topics:

SectionObjectives
Data Governance- Unity Catalog Permissions
  • 1. Understand the Unity Catalog permission inheritance model
    - Metadata and Discoverability
    • 1. Create and maintain descriptions and metadata for enterprise data
      Data Sharing and Federation- Delta Sharing
      • 1. Configure Databricks-to-Databricks Sharing
        • 2. Share live Lakehouse data with external computing platforms
          • 3. Configure sharing with external platforms using the open sharing protocol
            - Lakehouse Federation
            • 1. Configure Lakehouse Federation with appropriate governance
              Data Transformation, Cleansing, and Quality- Data Quality
              • 1. Develop data quarantining processes for invalid data
                • 2. Apply data quality controls using Lakeflow Spark Declarative Pipelines or Auto Loader
                  - Advanced Data Transformation
                  • 1. Apply window functions, joins, and aggregations to large datasets
                    • 2. Write efficient Spark SQL and PySpark transformations
                      Data Modelling- Scalable Data Models
                      • 1. Understand Liquid Clustering versus partitioning and Z-Ordering
                        • 2. Optimize data layout using Liquid Clustering
                          • 3. Design and implement scalable data models using Delta Lake
                            - Dimensional Modelling
                            • 1. Design dimensional models for analytical workloads
                              Developing Code for Data Processing using Python and SQL- Building and Testing ETL Pipelines
                              • 1. Configure environments, dependencies, memory, and retry behavior
                                • 2. Use control flow operators in pipeline components
                                  • 3. Create and automate ETL workloads using Jobs through UI, APIs, and CLI
                                    • 4. Build production-ready batch and streaming pipelines using Lakeflow Spark Declarative Pipelines and Auto Loader
                                      • 5. Use APPLY CHANGES APIs for change data capture
                                        • 6. Compare streaming tables and materialized views
                                          • 7. Develop unit and integration tests for data processing code
                                            • 8. Compare Spark Structured Streaming and Lakeflow Spark Declarative Pipelines
                                              - Using Python and Tools for Development
                                              • 1. Design and implement scalable Python project structures optimized for Databricks Asset Bundles
                                                • 2. Develop User-Defined Functions using Pandas/Python UDFs
                                                  • 3. Manage and troubleshoot third-party library installations and dependencies
                                                    Cost & Performance Optimisation- Delta Optimization
                                                    • 1. Apply data skipping and file pruning techniques
                                                      • 2. Use Change Data Feed to address streaming table limitations and improve latency
                                                        • 3. Understand deletion vectors and liquid clustering
                                                          - Query Performance
                                                          • 1. Identify inefficient joins and excessive data shuffling
                                                            • 2. Use Query Profile to identify performance bottlenecks
                                                              - Cost Optimization
                                                              • 1. Understand how Unity Catalog managed tables reduce operational overhead
                                                                Monitoring and Alerting- Alerting
                                                                • 1. Use SQL Alerts for data quality monitoring
                                                                  • 2. Configure Lakeflow Jobs notifications for job status and performance issues
                                                                    - Monitoring
                                                                    • 1. Use Databricks REST APIs and CLI for monitoring jobs and pipelines
                                                                      • 2. Use Query Profiler and Spark UI to monitor workloads
                                                                        • 3. Use Lakeflow Spark Declarative Pipelines event logs for monitoring
                                                                          • 4. Use system tables for resource, cost, audit, and workload monitoring
                                                                            Ensuring Data Security and Compliance- Compliance
                                                                            • 1. Develop data purging solutions according to data retention policies
                                                                              • 2. Implement pipelines that detect and mask personally identifiable information
                                                                                - Data Security
                                                                                • 1. Use row filters and column masks for sensitive data
                                                                                  • 2. Apply anonymization and pseudonymization techniques
                                                                                    • 3. Use ACLs to secure workspace objects and enforce least privilege
                                                                                      Debugging and Deploying- Debugging and Troubleshooting
                                                                                      • 1. Analyze errors and remediate failed job runs
                                                                                        • 2. Use Spark UI, cluster logs, system tables, and query profiles for diagnostics
                                                                                          • 3. Use Lakeflow Spark Declarative Pipelines event logs and Spark UI for debugging
                                                                                            - Deploying CI/CD
                                                                                            • 1. Integrate Git-based CI/CD workflows using Databricks Git Folders
                                                                                              • 2. Build and deploy Databricks resources using Databricks Asset Bundles
                                                                                                Data Ingestion & Acquisition- Design and implement data ingestion pipelines
                                                                                                • 1. Ingest Delta Lake, Parquet, ORC, Avro, JSON, CSV, XML, Text, and Binary data
                                                                                                  • 2. Ingest data from message buses and cloud storage
                                                                                                    • 3. Build append-only pipelines for batch and streaming data using Delta

                                                                                                      Databricks Certified Data Engineer Professional Sample Questions:

                                                                                                      Question 1

                                                                                                      The business intelligence team has a dashboard configured to track various summary metrics for retail stories. This includes total sales for the previous day alongside totals and averages for a variety of time periods. The fields required to populate this dashboard have the following schema:

                                                                                                      For Demand forecasting, the Lakehouse contains a validated table of all itemized sales updated incrementally in near real-time. This table named products_per_order, includes the following fields:

                                                                                                      Because reporting on long-term sales trends is less volatile, analysts using the new dashboard only require data to be refreshed once daily. Because the dashboard will be queried interactively by many users throughout a normal business day, it should return results quickly and reduce total compute associated with each materialization.
                                                                                                      Which solution meets the expectations of the end users while controlling and limiting possible costs?

                                                                                                      A. Use the Delta Cache to persists the products_per_order table in memory to quickly the dashboard with each query.
                                                                                                      B. Configure a webhook to execute an incremental read against products_per_order each time the dashboard is refreshed.
                                                                                                      C. Populate the dashboard by configuring a nightly batch job to save the required values as a table overwritten with each update.
                                                                                                      D. Define a view against the products_per_order table and define the dashboard against this view.
                                                                                                      E. Use Structure Streaming to configure a live dashboard against the products_per_order table within a Databricks notebook.


                                                                                                      Question 2

                                                                                                      A platform engineer needs to report the resource consumption, categorized by SKU tier, across all workspaces. The engineer decides to use the system.billing.usage system table to create a query. Which SQL query will accurately return the daily usage by product?

                                                                                                      A.

                                                                                                      B.

                                                                                                      C.

                                                                                                      D.


                                                                                                      Question 3

                                                                                                      A workspace admin has created a new catalog called finance_data and wants to delegate permission management to a finance team lead without giving them full admin rights. Which privilege should be granted to the finance team lead?

                                                                                                      A. MANAGE privilege on the finance_data catalog.
                                                                                                      B. Make the finance team lead a metastore admin.
                                                                                                      C. GRANT OPTION privilege on the finance_data catalog.
                                                                                                      D. ALL PRIVILEGES on the finance_data catalog.


                                                                                                      Question 4

                                                                                                      A data engineer is configuring a pipeline that will potentially see late-arriving, duplicate records.
                                                                                                      In addition to de-duplicating records within the batch, which of the following approaches allows the data engineer to deduplicate data against previously processed records as it is inserted into a Delta table?

                                                                                                      A. Perform a full outer join on a unique key and overwrite existing data.
                                                                                                      B. Rely on Delta Lake schema enforcement to prevent duplicate records.
                                                                                                      C. VACUUM the Delta table after each batch completes.
                                                                                                      D. Perform an insert-only merge with a matching condition on a unique key.
                                                                                                      E. Set the configuration delta.deduplicate = true.


                                                                                                      Question 5

                                                                                                      A platform team is creating a standardized template for Databricks Asset Bundles to support CI/CD. The template must specify defaults for artifacts, workspace root paths, and a run identity, while allowing a "dev" target to be the default and override specific paths. How should the team use databricks.yml to satisfy these requirements?

                                                                                                      A. Use deployment, builds, context, identity, and environments; set dev as default environment and override paths under builds.
                                                                                                      B. Use bundle, artifacts, workspace, run_as, and targets at the top level; set one target with default:true and override workspace paths or artifacts under that target.
                                                                                                      C. Use roots, modules, profiles, actor, and targets; where profiles contain workspace and artifacts defaults and actor sets run identity.
                                                                                                      D. Use project, packages, environment, identity, and stages; set dev as default stage and override workspace under environment.


                                                                                                      Solutions:

                                                                                                      Question 1
                                                                                                      Answer: C
                                                                                                      Question 2
                                                                                                      Answer: A
                                                                                                      Question 3
                                                                                                      Answer: A
                                                                                                      Question 4
                                                                                                      Answer: D
                                                                                                      Question 5
                                                                                                      Answer: B

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