Databricks Certified-Data-Engineer-Professional valid exam dumps : Databricks Certified Data Engineer Professional

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

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

SectionObjectives
Topic 1: Ensuring Data Security and Compliance- 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
        - Ensuring Compliance
        • 1. Implement compliant batch and streaming pipelines that detect and mask PII
          • 2. Develop data purging solutions that comply with data retention policies
            Topic 2: 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
                Topic 3: Cost & Performance Optimization- Optimize cost and performance
                • 1. Understand how and why Unity Catalog managed tables reduce operational overhead and maintenance burden
                  • 2. Understand Databricks query optimization techniques for large datasets, including data skipping and file pruning
                    • 3. Use query profiling to identify bottlenecks such as inefficient joins and data shuffling
                      • 4. Understand Delta optimization techniques such as deletion vectors and liquid clustering
                        • 5. Apply Change Data Feed to address streaming table limitations and improve latency
                          Topic 4: Debugging and Deploying- Debugging and Troubleshooting
                          • 1. Use Lakeflow Declarative Pipelines event logs and Spark UI to debug Lakeflow Declarative Pipelines and Spark pipelines
                            • 2. Identify diagnostic information using Spark UI, cluster logs, system tables, and query profiles to troubleshoot errors
                              • 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 5: Data Ingestion & Acquisition- Design and implement data ingestion pipelines
                                    • 1. Create an append-only data pipeline capable of handling both batch and streaming data using Delta
                                      • 2. Ingest formats including Delta Lake, Parquet, ORC, AVRO, JSON, CSV, XML, text, and binary data from sources such as message buses and cloud storage
                                        Topic 6: Developing Code for Data Processing using Python and SQL- Building and Testing an ETL Pipeline with Lakeflow Declarative Pipelines, SQL, and Apache Spark
                                        • 1. Build and manage reliable, production-ready batch and streaming data pipelines using Lakeflow Declarative Pipelines and Auto Loader
                                          • 2. Develop unit and integration tests using assertDataFrameEqual, assertSchemaEqual, DataFrame.transform, testing frameworks, and debugging tools
                                            • 3. Explain the advantages and disadvantages of streaming tables compared to materialized views
                                              • 4. Compare Spark Structured Streaming and Lakeflow Declarative Pipelines to determine the optimal approach for scalable ETL pipelines
                                                • 5. Create pipeline components using control flow operators such as if/else and foreach
                                                  • 6. Create and automate ETL workloads using Jobs through the UI, APIs, or CLI
                                                    • 7. Choose appropriate configurations for environments, dependencies, high-memory notebook tasks, and retry behavior
                                                      • 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. Manage and troubleshoot external third-party library installations and dependencies, including PyPI packages, local wheels, and source archives
                                                            • 3. Develop User-Defined Functions using Pandas/Python UDF
                                                              Topic 7: Data Transformation, Cleansing, and Quality- Transform and validate data
                                                              • 1. Develop a quarantining process for bad data with Lakeflow Declarative Pipelines or Auto Loader in classic jobs
                                                                • 2. Write efficient Spark SQL and PySpark code for advanced transformations including window functions, joins, and aggregations
                                                                  Topic 8: Data Modeling- Design and optimize data models
                                                                  • 1. Simplify data layout decisions and optimize query performance using liquid clustering
                                                                    • 2. Identify the benefits of liquid clustering over partitioning and Z-Ordering
                                                                      • 3. Design dimensional models for analytical workloads with efficient querying and aggregation
                                                                        • 4. Design and implement scalable data models using Delta Lake to manage large datasets
                                                                          Topic 9: Monitoring and Alerting- Alerting
                                                                          • 1. Use the Workflows UI and Jobs API to configure notifications for job status and performance issues
                                                                            • 2. Use SQL Alerts to monitor data quality
                                                                              - Monitoring
                                                                              • 1. Use Lakeflow Declarative Pipelines event logs to monitor pipelines
                                                                                • 2. Use Databricks REST APIs and Databricks CLI to monitor jobs and pipelines
                                                                                  • 3. Use Query Profile and Spark UI to monitor workloads
                                                                                    • 4. Use system tables for observability of resource utilization, cost, auditing, and workloads
                                                                                      Topic 10: 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. Use Delta Sharing to share live data from the Lakehouse with any computing platform
                                                                                          • 3. Configure Lakehouse Federation with appropriate governance across supported source systems

                                                                                            Databricks Certified Data Engineer Professional Sample Questions:

                                                                                            1. A data engineer is building a streaming data pipeline to ingest JSON files from cloud storage into a Delta Lake table. The pipeline must process files incrementally, handle schema evolution automatically, ensure exactly-once processing, and minimize manual infrastructure management.
                                                                                            How should the data engineer fulfill these requirements?

                                                                                            A) Use Auto Loader in batch mode with a daily job to overwrite the Delta table.
                                                                                            B) Use Lakeflow Spart Declarative Pipelines with Auto Loader and enabling schema inference with
                                                                                            "cloudFiles.schemaEvolutionMode"= "addNewColumns"
                                                                                            C) Use traditional Spark Structured Streaming with Auto Loader, manually configuring checkpoints location and enabling schema inference with "mergeSchema"= "true"
                                                                                            D) Use Lakeflow Spark Declarative Pipelines with a static DataFrame read, merge schema with spark.conf.set ("spark.databricks.delta.schema.autoMerge.enabled", "true")


                                                                                            2. A developer has successfully configured their credentials for Databricks Repos and cloned a remote Git repository. They do not have privileges to make changes to the main branch, which is the only branch currently visible in their workspace. Which approach allows this user to share their code updates without the risk of overwriting the work of their teammates?

                                                                                            A) Use repos to create a fork of the remote repository commit all changes and make a pull request on the source repository
                                                                                            B) Use repos to merge all difference and make a pull request back to the remote repository.
                                                                                            C) Use Repos to merge all differences and make a pull request back to the remote repository.
                                                                                            D) Use Repos to create a new branch commit all changes and push changes to the remote Git repertory.
                                                                                            E) Use Repos to pull changes from the remote Git repository; commit and push changes to a branch that appeared as changes were pulled.


                                                                                            3. A data team is working to optimize an existing large, fast-growing table 'orders' with high cardinality columns, which experiences significant data skew and requires frequent concurrent writes. The team notice that the columns 'user_id', 'event_timestamp' and 'product_id' are heavily used in analytical queries and filters, although those keys may be subject to change in the future due to different business requirements. Which partitioning strategy should the team choose to optimize the table for immediate data skipping, incremental management over time, and flexibility?

                                                                                            A) Z-order the table with OPTIMIZE orders ZORDER BY (user_id, product_id, event_timestamp)
                                                                                            B) Partition the table with: ALTER TABLE orders PARTITION BY user_id, product_id, event_timestamp
                                                                                            C) Cluster the table with: ALTER TABLE orders CLUSTER BY user_id, product_id, event_timestamp
                                                                                            D) Use z-order after partitiing the table: OPTIMIZE orders ZORDER BY (user_id, product_id) WHERE event_timestamp = current date () - 1 DAY


                                                                                            4. 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)


                                                                                            5. A junior data engineer has configured a workload that posts the following JSON to the Databricks REST API endpoint 2.0/jobs/create.

                                                                                            Assuming that all configurations and referenced resources are available, which statement describes the result of executing this workload three times?

                                                                                            A) The logic defined in the referenced notebook will be executed three times on new clusters with the configurations of the provided cluster ID.
                                                                                            B) Three new jobs named "Ingest new data" will be defined in the workspace, and they will each run once daily.
                                                                                            C) The logic defined in the referenced notebook will be executed three times on the referenced existing all purpose cluster.
                                                                                            D) One new job named "Ingest new data" will be defined in the workspace, but it will not be executed.
                                                                                            E) Three new jobs named "Ingest new data" will be defined in the workspace, but no jobs will be executed.


                                                                                            Solutions:

                                                                                            Question # 1
                                                                                            Answer: B
                                                                                            Question # 2
                                                                                            Answer: D
                                                                                            Question # 3
                                                                                            Answer: A
                                                                                            Question # 4
                                                                                            Answer: B
                                                                                            Question # 5
                                                                                            Answer: E

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