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  • Certified-Data-Engineer-Professional pdf
  • Exam Code: Certified-Data-Engineer-Professional
  • Exam Name: Databricks Certified Data Engineer Professional
  • Updated: Nov 07, 2026
  • Q & A: 250 Questions and Answers
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Databricks Certified-Data-Engineer-Professional Exam Syllabus Topics:

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

                                                                                                      Databricks Certified Data Engineer Professional Sample Questions:

                                                                                                      Question #1

                                                                                                      The data governance team has instituted a requirement that all tables containing Personal Identifiable Information (PH) must be clearly annotated. This includes adding column comments, table comments, and setting the custom table property "contains_pii" = true.
                                                                                                      The following SQL DDL statement is executed to create a new table:

                                                                                                      Which command allows manual confirmation that these three requirements have been met?

                                                                                                      • A. SHOW TABLES dev
                                                                                                      • B. DESCRIBE HISTORY dev.pii test
                                                                                                      • C. DESCRIBE EXTENDED dev.pii test
                                                                                                      • D. SHOW TBLPROPERTIES dev.pii test
                                                                                                      • E. DESCRIBE DETAIL dev.pii test
                                                                                                      Reveal Solution  Discussion  0

                                                                                                      Correct Answer: C  🗳️

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

                                                                                                      Question #2

                                                                                                      How are the operational aspects of Lakeflow Declarative Pipelines different from Spark Structured Streaming?

                                                                                                      • A. Structured Streaming can process continuous data streams, while Lakeflow Declarative Pipelines cannot.
                                                                                                      • B. Lakeflow Declarative Pipelines automatically handle schema evolution, while Structured Streaming always requires manual schema management.
                                                                                                      • C. Lakeflow Declarative Pipelines can write to Delta Lake format, while Structured Streaming cannot.
                                                                                                      • D. Lakeflow Declarative Pipelines manage the orchestration of multi-stage pipelines automatically, while Structured Streaming requires external orchestration for complex dependencies.
                                                                                                      Reveal Solution  Discussion  0

                                                                                                      Correct Answer: D  🗳️

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

                                                                                                      Question #3

                                                                                                      Which method can be used to determine the total wall-clock time it took to execute a query?

                                                                                                      • A. In the Spark UI, take the sum of all task durations that ran across all stages for all jobs associated with that query.
                                                                                                      • B. Open the Query Profiler associated with that query and use the Aggregated task time metric.
                                                                                                      • C. Open the Query Profiler associated with that query and use the Total wall-clock duration metric.
                                                                                                      • D. In the Spark UI, take the job duration of the longest-running job associated with that query.
                                                                                                      Reveal Solution  Discussion  0

                                                                                                      Correct Answer: C  🗳️

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

                                                                                                      Question #4

                                                                                                      A Data Engineer is building a simple data pipeline using Lakeflow Declarative Pipelines (LDP) in Databricks to ingest customer data. The raw customer data is stored in a cloud storage location in JSON format. The task is to create Lakeflow Declarative Pipelines that read the raw JSON data and write it into a Delta table for further processing. Which code snippet will correctly ingest the raw JSON data and create a Delta table using LDP?

                                                                                                      • A. import dlt
                                                                                                        @dlt.table
                                                                                                        def raw_customers():
                                                                                                        return spark.read.json("s3://my-bucket/raw-customers/")
                                                                                                      • B. import dlt
                                                                                                        @dlt.table
                                                                                                        def raw_customers():
                                                                                                        return spark.read.format("parquet").load("s3://my-bucket/raw-customers/")
                                                                                                      • C. import dlt
                                                                                                        @dlt.view
                                                                                                        def raw_customers():
                                                                                                        return spark.format.json("s3://my-bucket/raw-customers/")
                                                                                                      • D. import dlt
                                                                                                        @dlt.table
                                                                                                        def raw_customers():
                                                                                                        return spark.read.format("csv").load("s3://my-bucket/raw-customers/")
                                                                                                      Reveal Solution  Discussion  0

                                                                                                      Correct Answer: A  🗳️

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

                                                                                                      Question #5

                                                                                                      Each configuration below is identical to the extent that each cluster has 400 GB total of RAM 160 total cores and only one Executor per VM.
                                                                                                      Given an extremely long-running job for which completion must be guaranteed, which cluster configuration will be able to guarantee completion of the job in light of one or more VM failures?

                                                                                                      • A. - Total VMs: 8
                                                                                                        - 50 GB per Executor
                                                                                                        - 20 Cores / Executor
                                                                                                      • B. - Total VMs: 2
                                                                                                        - 200 GB per Executor
                                                                                                        - 80 Cores / Executor
                                                                                                      • C. - Total VMs: 16
                                                                                                        - 25 GB per Executor
                                                                                                        - 10 Cores / Executor
                                                                                                      • D. - Total VMs: 4
                                                                                                        - 100 GB per Executor
                                                                                                        - 40 Cores / Executor
                                                                                                      • E. - Total VMs: 1
                                                                                                        - 400 GB per Executor
                                                                                                        - 160 Cores/Executor
                                                                                                      Reveal Solution  Discussion  0

                                                                                                      Correct Answer: C  🗳️

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