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

SectionObjectives
Data Governance- Govern enterprise data
  • 1. Demonstrate understanding of the Unity Catalog permission inheritance model
    • 2. Create and add descriptions and metadata to enterprise data to improve discoverability
      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. Configure Lakehouse Federation with appropriate governance across supported source systems
          • 3. Use Delta Sharing to share live data from the Lakehouse with any computing platform
            Cost & Performance Optimization- Optimize cost and performance
            • 1. Understand how and why Unity Catalog managed tables reduce operational overhead and maintenance burden
              • 2. Use query profiling to identify bottlenecks such as inefficient joins and data shuffling
                • 3. Understand Databricks query optimization techniques for large datasets, including data skipping and file pruning
                  • 4. Apply Change Data Feed to address streaming table limitations and improve latency
                    • 5. Understand Delta optimization techniques such as deletion vectors and liquid clustering
                      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
                          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
                              Debugging and Deploying- Deploying CI/CD
                              • 1. Build and deploy Databricks resources using Databricks Asset Bundles
                                • 2. Configure and integrate Git-based CI/CD workflows using Databricks Git folders for notebook and code deployment
                                  - Debugging and Troubleshooting
                                  • 1. Use Lakeflow Declarative Pipelines event logs and Spark UI to debug Lakeflow Declarative Pipelines and Spark pipelines
                                    • 2. Analyze errors and remediate failed job runs using job repairs and parameter overrides
                                      • 3. Identify diagnostic information using Spark UI, cluster logs, system tables, and query profiles to troubleshoot errors
                                        Monitoring and Alerting- Monitoring
                                        • 1. Use Databricks REST APIs and Databricks CLI to monitor jobs and pipelines
                                          • 2. Use Query Profile and Spark UI to monitor workloads
                                            • 3. Use system tables for observability of resource utilization, cost, auditing, and workloads
                                              • 4. Use Lakeflow Declarative Pipelines event logs to monitor pipelines
                                                - Alerting
                                                • 1. Use SQL Alerts to monitor data quality
                                                  • 2. Use the Workflows UI and Jobs API to configure notifications for job status and performance issues
                                                    Developing Code for Data Processing using Python and SQL- Building and Testing an ETL Pipeline with Lakeflow Declarative Pipelines, SQL, and Apache Spark
                                                    • 1. Choose appropriate configurations for environments, dependencies, high-memory notebook tasks, and retry behavior
                                                      • 2. Create pipeline components using control flow operators such as if/else and foreach
                                                        • 3. Develop unit and integration tests using assertDataFrameEqual, assertSchemaEqual, DataFrame.transform, testing frameworks, and debugging tools
                                                          • 4. Explain the advantages and disadvantages of streaming tables compared to materialized views
                                                            • 5. Build and manage reliable, production-ready batch and streaming data pipelines using Lakeflow Declarative Pipelines and Auto Loader
                                                              • 6. Compare Spark Structured Streaming and Lakeflow Declarative Pipelines to determine the optimal approach for scalable ETL pipelines
                                                                • 7. Create and automate ETL workloads using Jobs through the UI, APIs, or CLI
                                                                  • 8. Use APPLY CHANGES APIs to simplify CDC in Lakeflow Declarative Pipelines
                                                                    - Using Python and Tools for Development
                                                                    • 1. Manage and troubleshoot external third-party library installations and dependencies, including PyPI packages, local wheels, and source archives
                                                                      • 2. Design and implement a scalable Python project structure optimized for Databricks Asset Bundles, enabling modular development, deployment automation, and CI/CD integration
                                                                        • 3. Develop User-Defined Functions using Pandas/Python UDF
                                                                          Data Modeling- Design and optimize data models
                                                                          • 1. Design dimensional models for analytical workloads with efficient querying and aggregation
                                                                            • 2. Identify the benefits of liquid clustering over partitioning and Z-Ordering
                                                                              • 3. Design and implement scalable data models using Delta Lake to manage large datasets
                                                                                • 4. Simplify data layout decisions and optimize query performance using liquid clustering
                                                                                  Ensuring Data Security and Compliance- Ensuring Compliance
                                                                                  • 1. Develop data purging solutions that comply with data retention policies
                                                                                    • 2. Implement compliant batch and streaming pipelines that detect and mask PII
                                                                                      - Applying Data Security Mechanisms
                                                                                      • 1. Apply anonymization and pseudonymization methods including hashing, tokenization, suppression, and generalization
                                                                                        • 2. Use row filters and column masks to protect sensitive table data
                                                                                          • 3. Use ACLs to secure workspace objects and enforce the principle of least privilege

                                                                                            Databricks Certified Data Engineer Professional Sample Questions:

                                                                                            1. The Databricks workspace administrator has configured interactive clusters for each of the data engineering groups. To control costs, clusters are set to terminate after 30 minutes of inactivity.
                                                                                            Each user should be able to execute workloads against their assigned clusters at any time of the day.
                                                                                            Assuming users have been added to a workspace but not granted any permissions, which of the following describes the minimal permissions a user would need to start and attach to an already configured cluster.

                                                                                            A) "Can Manage" privileges on the required cluster
                                                                                            B) Workspace Admin privileges, cluster creation allowed. "Can Attach To" privileges on the required cluster
                                                                                            C) "Can Restart" privileges on the required cluster
                                                                                            D) Cluster creation allowed. "Can Attach To" privileges on the required cluster
                                                                                            E) Cluster creation allowed. "Can Restart" privileges on the required cluster


                                                                                            2. The data architect has mandated that all tables in the Lakehouse should be configured as external Delta Lake tables.
                                                                                            Which approach will ensure that this requirement is met?

                                                                                            A) Whenever a database is being created, make sure that the location keyword is used
                                                                                            B) When tables are created, make sure that the external keyword is used in the create table statement.
                                                                                            C) When the workspace is being configured, make sure that external cloud object storage has been mounted.
                                                                                            D) Whenever a table is being created, make sure that the location keyword is used.
                                                                                            E) When configuring an external data warehouse for all table storage. leverage Databricks for all ELT.


                                                                                            3. A data engineer needs to design an efficient pipeline that automatically processes new CSV files as they arrive in S3 storage. Which Databricks feature should the data engineer use to meet these requirements?

                                                                                            A) Traditional batch processing with scheduled Databricks Jobs
                                                                                            B) COPY INTO SQL command with parameters to track processed files
                                                                                            C) Streaming from cloud storage using standard Spark readStream with format ("csv") and format ("json")
                                                                                            D) Auto Loader with schema inference and evolution enabled


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


                                                                                            5. A workspace admin has created a new catalog called finance_data and wants to delegate permission management to a finance team lead without giving them full admin rights. Which privilege should be granted to the finance team lead?

                                                                                            A) GRANT OPTION privilege on the finance_data catalog.
                                                                                            B) MANAGE privilege on the finance_data catalog.
                                                                                            C) ALL PRIVILEGES on the finance_data catalog.
                                                                                            D) Make the finance team lead a metastore admin.


                                                                                            Solutions:

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

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