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

SectionObjectives
Debugging and Deploying- 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
        - Deploying CI/CD
        • 1. Build and deploy Databricks resources using Databricks Asset Bundles
          • 2. Integrate Git-based CI/CD workflows using Databricks Git Folders
            Monitoring and Alerting- Alerting
            • 1. Configure Lakeflow Jobs notifications for job status and performance issues
              • 2. Use SQL Alerts for data quality monitoring
                - Monitoring
                • 1. Use Query Profiler and Spark UI to monitor workloads
                  • 2. Use Databricks REST APIs and CLI for monitoring jobs and pipelines
                    • 3. Use system tables for resource, cost, audit, and workload monitoring
                      • 4. Use Lakeflow Spark Declarative Pipelines event logs for monitoring
                        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
                                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
                                      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 Sharing and Federation- 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
                                                - Lakehouse Federation
                                                • 1. Configure Lakehouse Federation with appropriate governance
                                                  Developing Code for Data Processing using Python and SQL- Building and Testing ETL Pipelines
                                                  • 1. Compare streaming tables and materialized views
                                                    • 2. Create and automate ETL workloads using Jobs through UI, APIs, and CLI
                                                      • 3. Build production-ready batch and streaming pipelines using Lakeflow Spark Declarative Pipelines and Auto Loader
                                                        • 4. Use control flow operators in pipeline components
                                                          • 5. Configure environments, dependencies, memory, and retry behavior
                                                            • 6. Use APPLY CHANGES APIs for change data capture
                                                              • 7. Develop unit and integration tests for data processing code
                                                                • 8. Compare Spark Structured Streaming and Lakeflow Spark Declarative Pipelines
                                                                  - 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
                                                                        Cost & Performance Optimisation- Query Performance
                                                                        • 1. Identify inefficient joins and excessive data shuffling
                                                                          • 2. Use Query Profile to identify performance bottlenecks
                                                                            - 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
                                                                                  - Cost Optimization
                                                                                  • 1. Understand how Unity Catalog managed tables reduce operational overhead
                                                                                    Ensuring Data Security and Compliance- Data Security
                                                                                    • 1. Apply anonymization and pseudonymization techniques
                                                                                      • 2. Use ACLs to secure workspace objects and enforce least privilege
                                                                                        • 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
                                                                                              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. Optimize data layout using Liquid Clustering
                                                                                                    • 3. Understand Liquid Clustering versus partitioning and Z-Ordering

                                                                                                      Databricks Certified Data Engineer Professional Sample Questions:

                                                                                                      Predictive Optimization is an automated Databricks service enabled by default for Unity Catalog Managed tables. It helps maintain Delta tables by continuously optimizing them to ensure optimal performance and costs. Which two operations does Predictive Optimization run to maintain the Delta tables? (Choose two.)

                                                                                                      • A. COMPACT
                                                                                                      • B. PARTITION BY
                                                                                                      • C. BUCKETING
                                                                                                      • D. OPTIMIZE
                                                                                                      • E. ANALYZE
                                                                                                      Answer: D,E

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                                                                                                      A data engineer is analyzing transactional data in a PySpark DataFrame df containing customer_id, transaction_timestamp (precise to milliseconds), and amount_spent. The objective is to compute a cumulative sum of amount_spent per customer, strictly ordered by transaction_timestamp. The cumulative sum must include all transactions from the earliest timestamp up to and including the current row, respecting temporal ordering within each customer partition. Which PySpark code snippet most accurately constructs the appropriate window specification and applies the aggregation to yield the correct cumulative expenditure per customer?

                                                                                                      • A.
                                                                                                      • B.
                                                                                                      • C.
                                                                                                      • D.
                                                                                                      Answer: B

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                                                                                                      A small company based in the United States has recently contracted a consulting firm in India to implement several new data engineering pipelines to power artificial intelligence applications. All the company's data is stored in regional cloud storage in the United States.
                                                                                                      The workspace administrator at the company is uncertain about where the Databricks workspace used by the contractors should be deployed.
                                                                                                      Assuming that all data governance considerations are accounted for, which statement accurately informs this decision?

                                                                                                      • A. Databricks notebooks send all executable code from the user's browser to virtual machines over the open internet; whenever possible, choosing a workspace region near the end users is the most secure.
                                                                                                      • B. Cross-region reads and writes can incur significant costs and latency; whenever possible, compute should be deployed in the same region the data is stored.
                                                                                                      • C. Databricks workspaces do not rely on any regional infrastructure; as such, the decision should be made based upon what is most convenient for the workspace administrator.
                                                                                                      • D. Databricks runs HDFS on cloud volume storage; as such, cloud virtual machines must be deployed in the region where the data is stored.
                                                                                                      • E. Databricks leverages user workstations as the driver during interactive development; as such, users should always use a workspace deployed in a region they are physically near.
                                                                                                      Answer: B

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

                                                                                                      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. 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.
                                                                                                      • C. The versions of each source table will be stored in the table transaction log; query results will be saved to DBFS with each query.
                                                                                                      • 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.
                                                                                                      Answer: A

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                                                                                                      A data company uses Databricks Unity Catalog and has multiple enterprise data sources, including PostgreSQL, Snowflake, and SQL Server. The central data platform team wants to configure Lakehouse Federation so analysts can query external tables directly in Databricks using Databricks SQL, without duplicating data. Which steps are necessary to configure Lakehouse Federation in a secure and governed manner?

                                                                                                      • A. Configure connections and foreign catalog in Unity Catalog, then grant access to foreign catalogs, schemas, and tables using Unity Catalog permissions.
                                                                                                      • B. Create external locations and storage credentials to connect to each database, then register foreign tables in Unity Catalog.
                                                                                                      • C. Mirror the external datasets into Delta Lake using Auto Loader, and govern them using Data Lineage and System Tables.
                                                                                                      • D. Use Partner Connect to create linked datasets, and apply table ACLs at the source system to govern access through Databricks.
                                                                                                      Answer: A

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

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