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

SectionObjectives
Topic 1: 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 2: Cost & Performance Optimisation- 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
            - 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
                  Topic 3: Ensuring Data Security and Compliance- Compliance
                  • 1. Implement pipelines that detect and mask personally identifiable information
                    • 2. Develop data purging solutions according to data retention policies
                      - 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
                            Topic 4: 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. Write efficient Spark SQL and PySpark transformations
                                  • 2. Apply window functions, joins, and aggregations to large datasets
                                    Topic 5: Data Ingestion & Acquisition- Design and implement data ingestion pipelines
                                    • 1. Build append-only pipelines for batch and streaming data using Delta
                                      • 2. Ingest Delta Lake, Parquet, ORC, Avro, JSON, CSV, XML, Text, and Binary data
                                        • 3. Ingest data from message buses and cloud storage
                                          Topic 6: 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. Use Spark UI, cluster logs, system tables, and query profiles for diagnostics
                                                • 2. Analyze errors and remediate failed job runs
                                                  • 3. Use Lakeflow Spark Declarative Pipelines event logs and Spark UI for debugging
                                                    Topic 7: Data Sharing and Federation- Lakehouse Federation
                                                    • 1. Configure Lakehouse Federation with appropriate governance
                                                      - Delta Sharing
                                                      • 1. Configure sharing with external platforms using the open sharing protocol
                                                        • 2. Share live Lakehouse data with external computing platforms
                                                          • 3. Configure Databricks-to-Databricks Sharing
                                                            Topic 8: Data Modelling- 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
                                                                  - Dimensional Modelling
                                                                  • 1. Design dimensional models for analytical workloads
                                                                    Topic 9: Monitoring and Alerting- Monitoring
                                                                    • 1. Use Query Profiler and Spark UI to monitor workloads
                                                                      • 2. Use Lakeflow Spark Declarative Pipelines event logs for monitoring
                                                                        • 3. Use system tables for resource, cost, audit, and workload monitoring
                                                                          • 4. Use Databricks REST APIs and CLI for monitoring jobs and pipelines
                                                                            - Alerting
                                                                            • 1. Use SQL Alerts for data quality monitoring
                                                                              • 2. Configure Lakeflow Jobs notifications for job status and performance issues
                                                                                Topic 10: Developing Code for Data Processing using Python and SQL- Building and Testing ETL Pipelines
                                                                                • 1. Use APPLY CHANGES APIs for change data capture
                                                                                  • 2. Configure environments, dependencies, memory, and retry behavior
                                                                                    • 3. Use control flow operators in pipeline components
                                                                                      • 4. Create and automate ETL workloads using Jobs through UI, APIs, and CLI
                                                                                        • 5. Build production-ready batch and streaming pipelines using Lakeflow Spark Declarative Pipelines and Auto Loader
                                                                                          • 6. Compare Spark Structured Streaming and Lakeflow Spark Declarative Pipelines
                                                                                            • 7. Develop unit and integration tests for data processing code
                                                                                              • 8. Compare streaming tables and materialized views
                                                                                                - Using Python and Tools for Development
                                                                                                • 1. Manage and troubleshoot third-party library installations and dependencies
                                                                                                  • 2. Develop User-Defined Functions using Pandas/Python UDFs
                                                                                                    • 3. Design and implement scalable Python project structures optimized for Databricks Asset Bundles

                                                                                                      Databricks Certified Data Engineer Professional Sample Questions:

                                                                                                      1. A data engineer wants to ingest a large collection of image files (JPEG and PNG) from cloud object storage into a Unity Catalog-managed table for analysis and visualization. Which two configurations and practices are recommended to incrementally ingest these images into the table? (Choose two.)

                                                                                                      A) Move files to a volume and read with SQL editor.
                                                                                                      B) Use Auto Loader and set cloudFiles.format to "TEXT".
                                                                                                      C) Use the pathGlobFilter option to select only image files (e.g., "*.jpg,*.png").
                                                                                                      D) Use Auto Loader and set cloudFiles.format to "BINARYFILE".
                                                                                                      E) Use Auto Loader and set cloudFiles.format to "IMAGE".


                                                                                                      2. Which REST API call can be used to review the notebooks configured to run as tasks in a multi- task job?

                                                                                                      A) /jobs/runs/get-output
                                                                                                      B) /jobs/runs/list
                                                                                                      C) /jobs/get
                                                                                                      D) /jobs/runs/get
                                                                                                      E) /jobs/list


                                                                                                      3. A table is registered with the following code:

                                                                                                      Both users and orders are Delta Lake tables. Which statement describes the results of querying recent_orders?

                                                                                                      A) All logic will execute when the table is defined and store the result of joining tables to the DBFS; this stored data will be returned when the table is queried.
                                                                                                      B) The versions of each source table will be stored in the table transaction log; query results will be saved to DBFS with each query.
                                                                                                      C) All logic will execute at query time and return the result of joining the valid versions of the source tables at the time the query finishes.
                                                                                                      D) Results will be computed and cached when the table is defined; these cached results will incrementally update as new records are inserted into source tables.
                                                                                                      E) All logic will execute at query time and return the result of joining the valid versions of the source tables at the time the query began.


                                                                                                      4. A data engineering team is setting up a Git project to automate integration tests using Databricks Asset Bundles and the Git provider's CI/CD functionalities. When a pull containing changes to their pipleline is sent, they need to run a Job to test their data pipeline. What is the correct databricks bundle command sequence to be executed from the Git provider's CI/CD automation for this task?

                                                                                                      A) deploy, run, validate
                                                                                                      B) validate, deploy, run
                                                                                                      C) init, validate, deploy, run
                                                                                                      D) init, deploy, run, validate


                                                                                                      5. 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 Time Travel
                                                                                                      B) Delta VACUUM
                                                                                                      C) Delta APPEND_ONLY
                                                                                                      D) Delta OPTIMIZE


                                                                                                      Solutions:

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

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