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Exam Code: Certified-Data-Engineer-Professional
Exam Name: Databricks Certified Data Engineer Professional
Certification Provider: Databricks
Corresponding Certification: Databricks Certification
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Databricks Certified-Data-Engineer-Professional Exam Syllabus Topics:

SectionObjectives
Developing Code for Data Processing using Python and SQL- Building and Testing ETL Pipelines
  • 1. Build production-ready batch and streaming pipelines using Lakeflow Spark Declarative Pipelines and Auto Loader
    • 2. Use control flow operators in pipeline components
      • 3. Compare Spark Structured Streaming and Lakeflow Spark Declarative Pipelines
        • 4. Create and automate ETL workloads using Jobs through UI, APIs, and CLI
          • 5. Develop unit and integration tests for data processing code
            • 6. Use APPLY CHANGES APIs for change data capture
              • 7. Compare streaming tables and materialized views
                • 8. Configure environments, dependencies, memory, and retry behavior
                  - Using Python and Tools for Development
                  • 1. Manage and troubleshoot third-party library installations and dependencies
                    • 2. Design and implement scalable Python project structures optimized for Databricks Asset Bundles
                      • 3. Develop User-Defined Functions using Pandas/Python UDFs
                        Cost & Performance Optimisation- 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
                              - Delta Optimization
                              • 1. Use Change Data Feed to address streaming table limitations and improve latency
                                • 2. Understand deletion vectors and liquid clustering
                                  • 3. Apply data skipping and file pruning techniques
                                    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. Configure Databricks-to-Databricks Sharing
                                          • 3. Share live Lakehouse data with external computing platforms
                                            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
                                                  Data Governance- Metadata and Discoverability
                                                  • 1. Create and maintain descriptions and metadata for enterprise data
                                                    - Unity Catalog Permissions
                                                    • 1. Understand the Unity Catalog permission inheritance model
                                                      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
                                                              Debugging and Deploying- Deploying CI/CD
                                                              • 1. Integrate Git-based CI/CD workflows using Databricks Git Folders
                                                                • 2. Build and deploy Databricks resources using Databricks Asset Bundles
                                                                  - Debugging and Troubleshooting
                                                                  • 1. Use Spark UI, cluster logs, system tables, and query profiles for diagnostics
                                                                    • 2. Use Lakeflow Spark Declarative Pipelines event logs and Spark UI for debugging
                                                                      • 3. Analyze errors and remediate failed job runs
                                                                        Data Modelling- Dimensional Modelling
                                                                        • 1. Design dimensional models for analytical workloads
                                                                          - 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
                                                                                Monitoring and Alerting- 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
                                                                                        - Alerting
                                                                                        • 1. Use SQL Alerts for data quality monitoring
                                                                                          • 2. Configure Lakeflow Jobs notifications for job status and performance issues
                                                                                            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

                                                                                                      Databricks Certified Data Engineer Professional Sample Questions:

                                                                                                      A data engineering team is setting up deployment automation. To deploy workspace assets remotely using the Databricks CLI command, they must configure it with proper authentication.
                                                                                                      Which authentication approach will provide the highest level of security?

                                                                                                      • A. Use a service principal ID and its OAuth client secret.
                                                                                                      • B. Use a shared user account and its OAuth client secret.
                                                                                                      • C. Use a service principal and its Personal Access Token.
                                                                                                      • D. Use a service principal with OAuth token federation.
                                                                                                      Answer: D

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                                                                                                      A streaming video analytics team ingests billions of events daily into a Unity Catalog-managed Delta table video_events. Analysts run ad-hoc point-lookup queries on columns like user_id, campaign_id, and region. The team manually runs OPTIMIZE video_events ZORDER BY (user_id, campaign_id, region), but still sees poor performance on recent data and dislikes the operational overhead. The team wants a hands-off way to keep hot columns co-located as query patterns evolve. Which Delta capability should the team leverage on video_events?

                                                                                                      • A. Enable auto-compaction (optimizeWrite and autoCompact).
                                                                                                      • B. Enable Delta caching.
                                                                                                      • C. Utilize Liquid Clustering (CLUSTER BY AUTO) and Predictive Optimization.
                                                                                                      • D. Schedule OPTIMIZE/ZORDER to run after each job to improve recent file performance.
                                                                                                      Answer: C

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                                                                                                      Which statement describes integration testing?

                                                                                                      • A. Validates interactions between subsystems of your application
                                                                                                      • B. Validates behavior of individual elements of your application
                                                                                                      • C. Requires manual intervention
                                                                                                      • D. Requires an automated testing framework
                                                                                                      • E. Validates an application use case
                                                                                                      Answer: A

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

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                                                                                                      An analytics team wants to run a short-term experiment in Databricks SQL on the customer transactions Delta table (about 20 billion records) created by the data engineering team. Which strategy should the data engineering team use to ensure minimal downtime and no impact on the ongoing ETL processes?

                                                                                                      • A. Give the analytics team direct access to the production table.
                                                                                                      • B. Shallow clone the table for the analytics team.
                                                                                                      • C. Deep clone the table for the analytics team.
                                                                                                      • D. Create a new table for the analytics team using a CTAS statement.
                                                                                                      Answer: B

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