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

SectionObjectives
Topic 1: 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. Build and deploy Databricks resources using Databricks Asset Bundles
          • 2. Integrate Git-based CI/CD workflows using Databricks Git Folders
            Topic 2: Cost & Performance Optimisation- Delta Optimization
            • 1. Apply data skipping and file pruning techniques
              • 2. Understand deletion vectors and liquid clustering
                • 3. Use Change Data Feed to address streaming table limitations and improve latency
                  - Cost Optimization
                  • 1. Understand how Unity Catalog managed tables reduce operational overhead
                    - Query Performance
                    • 1. Identify inefficient joins and excessive data shuffling
                      • 2. Use Query Profile to identify performance bottlenecks
                        Topic 3: Data Transformation, Cleansing, and Quality- Advanced Data Transformation
                        • 1. Write efficient Spark SQL and PySpark transformations
                          • 2. Apply window functions, joins, and aggregations to large datasets
                            - Data Quality
                            • 1. Apply data quality controls using Lakeflow Spark Declarative Pipelines or Auto Loader
                              • 2. Develop data quarantining processes for invalid data
                                Topic 4: 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
                                      Topic 5: Developing Code for Data Processing using Python and SQL- Using Python and Tools for Development
                                      • 1. Design and implement scalable Python project structures optimized for Databricks Asset Bundles
                                        • 2. Manage and troubleshoot third-party library installations and dependencies
                                          • 3. Develop User-Defined Functions using Pandas/Python UDFs
                                            - Building and Testing ETL Pipelines
                                            • 1. Create and automate ETL workloads using Jobs through UI, APIs, and CLI
                                              • 2. Build production-ready batch and streaming pipelines using Lakeflow Spark Declarative Pipelines and Auto Loader
                                                • 3. Use control flow operators in pipeline components
                                                  • 4. Develop unit and integration tests for data processing code
                                                    • 5. Configure environments, dependencies, memory, and retry behavior
                                                      • 6. Compare streaming tables and materialized views
                                                        • 7. Use APPLY CHANGES APIs for change data capture
                                                          • 8. Compare Spark Structured Streaming and Lakeflow Spark Declarative Pipelines
                                                            Topic 6: 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
                                                                Topic 7: Data Modelling- Dimensional Modelling
                                                                • 1. Design dimensional models for analytical workloads
                                                                  - Scalable Data Models
                                                                  • 1. Optimize data layout using Liquid Clustering
                                                                    • 2. Understand Liquid Clustering versus partitioning and Z-Ordering
                                                                      • 3. Design and implement scalable data models using Delta Lake
                                                                        Topic 8: Monitoring and Alerting- Monitoring
                                                                        • 1. Use Lakeflow Spark Declarative Pipelines event logs for monitoring
                                                                          • 2. Use Databricks REST APIs and CLI for monitoring jobs and pipelines
                                                                            • 3. Use Query Profiler and Spark UI to monitor workloads
                                                                              • 4. Use system tables for resource, cost, audit, and workload monitoring
                                                                                - Alerting
                                                                                • 1. Use SQL Alerts for data quality monitoring
                                                                                  • 2. Configure Lakeflow Jobs notifications for job status and performance issues
                                                                                    Topic 9: Ensuring Data Security and Compliance- Data Security
                                                                                    • 1. Use ACLs to secure workspace objects and enforce least privilege
                                                                                      • 2. Apply anonymization and pseudonymization techniques
                                                                                        • 3. Use row filters and column masks for sensitive data
                                                                                          - Compliance
                                                                                          • 1. Develop data purging solutions according to data retention policies
                                                                                            • 2. Implement pipelines that detect and mask personally identifiable information
                                                                                              Topic 10: 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

                                                                                                      Databricks Certified Data Engineer Professional Sample Questions:

                                                                                                      Question 1

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


                                                                                                      Question 2

                                                                                                      A junior data engineer seeks to leverage Delta Lake's Change Data Feed functionality to create a Type 1 table representing all of the values that have ever been valid for all rows in a bronze table created with the property delta.enableChangeDataFeed = true. They plan to execute the following code as a daily job:

                                                                                                      Which statement describes the execution and results of running the above query multiple times?

                                                                                                      A. Each time the job is executed, the target table will be overwritten using the entire history of inserted or updated records, giving the desired result.
                                                                                                      B. Each time the job is executed, only those records that have been inserted or updated since the last execution will be appended to the target table giving the desired result.
                                                                                                      C. Each time the job is executed, the entire available history of inserted or updated records will be appended to the target table, resulting in many duplicate entries.
                                                                                                      D. Each time the job is executed, newly updated records will be merged into the target table, overwriting previous values with the same primary keys.
                                                                                                      E. Each time the job is executed, the differences between the original and current versions are calculated; this may result in duplicate entries for some records.


                                                                                                      Question 3

                                                                                                      A data engineer and a platform engineer are working together to automate their system tasks. A script needs to be executed outside of Databricks only if a particular daily Databricks job finishes successfully for the day. Databricks CLI command was used to check the last execution of the job. What are the required command options for that task?

                                                                                                      A. databricks jobs list-runs --job-id JOB_ID --start-time-to TODAY_MIDNIGHT_EPOCH_MS -- completed-only
                                                                                                      B. databricks jobs list-runs --job-id JOB_ID --start-time-from TODAY_MIDNIGHT_EPOCH_MS -- active-only
                                                                                                      C. databricks jobs list-runs --job-id JOB_ID --start-time-from TODAY_MIDNIGHT_EPOCH_MS -- completed-only
                                                                                                      D. databricks jobs list-runs --job-id JOB_ID --start-time-to TODAY_MIDNIGHT_EPOCH_MS --active- only


                                                                                                      Question 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.


                                                                                                      Question 5

                                                                                                      A data engineer is implementing Unity Catalog governance for a multi-team environment. Data scientists need interactive clusters for basic data exploration tasks, while automated ETL jobs require dedicated processing. How should the data engineer configure cluster isolation policies to enforce least privilege and ensure Unity Catalog compliance?

                                                                                                      A. Configure all clusters with NO ISOLATION_SHARED access mode since Unity Catalog works with any cluster configuration.
                                                                                                      B. Use only DEDICATED access mode for both interactive workloads and automated jobs to maximize security isolation.
                                                                                                      C. Allow all users to create any cluster type and rely on manual configuration to enable Unity Catalog access modes.
                                                                                                      D. Create compute policies with STANDARD access mode for interactive workloads and DEDICATED access mode for automated jobs.


                                                                                                      Solutions:

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

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