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

Section	Objectives
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. Apply window functions, joins, and aggregations to large datasets 2. Write efficient Spark SQL and PySpark transformations
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. Use Query Profile to identify performance bottlenecks 2. Identify inefficient joins and excessive data shuffling - Cost Optimization 1. Understand how Unity Catalog managed tables reduce operational overhead
Data Sharing and Federation	- Delta Sharing 1. Share live Lakehouse data with external computing platforms 2. Configure Databricks-to-Databricks Sharing 3. Configure sharing with external platforms using the open sharing protocol - Lakehouse Federation 1. Configure Lakehouse Federation with appropriate governance
Monitoring and Alerting	- Alerting 1. Use SQL Alerts for data quality monitoring 2. Configure Lakeflow Jobs notifications for job status and performance issues - 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 system tables for resource, cost, audit, and workload monitoring 4. Use Lakeflow Spark Declarative Pipelines event logs for monitoring
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
Ensuring Data Security and Compliance	- 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 - Compliance 1. Implement pipelines that detect and mask personally identifiable information 2. Develop data purging solutions according to data retention policies
Developing Code for Data Processing using Python and SQL	- Building and Testing ETL Pipelines 1. Compare streaming tables and materialized views 2. Use control flow operators in pipeline components 3. Configure environments, dependencies, memory, and retry behavior 4. Build production-ready batch and streaming pipelines using Lakeflow Spark Declarative Pipelines and Auto Loader 5. Create and automate ETL workloads using Jobs through UI, APIs, and CLI 6. Develop unit and integration tests for data processing code 7. Compare Spark Structured Streaming and Lakeflow Spark Declarative Pipelines 8. Use APPLY CHANGES APIs for change data capture - 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
Debugging and Deploying	- Debugging and Troubleshooting 1. Analyze errors and remediate failed job runs 2. Use Lakeflow Spark Declarative Pipelines event logs and Spark UI for debugging 3. Use Spark UI, cluster logs, system tables, and query profiles for diagnostics - Deploying CI/CD 1. Integrate Git-based CI/CD workflows using Databricks Git Folders 2. Build and deploy Databricks resources using Databricks Asset Bundles
Data Modelling	- Scalable Data Models 1. Design and implement scalable data models using Delta Lake 2. Understand Liquid Clustering versus partitioning and Z-Ordering 3. Optimize data layout using Liquid Clustering - Dimensional Modelling 1. Design dimensional models for analytical workloads
Data Ingestion & Acquisition	- Design and implement data ingestion pipelines 1. Ingest data from message buses and cloud storage 2. Ingest Delta Lake, Parquet, ORC, Avro, JSON, CSV, XML, Text, and Binary data 3. Build append-only pipelines for batch and streaming data using Delta

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Databricks Certified Data Engineer Professional Sample Questions (Q217-Q222):

NEW QUESTION # 217
A data engineer is using the AUTO CDC API in Lakeflow Spark Declarative Pipeline to propagate deletions from a source table (orders_source) to a target table (orders_target). The source has Change Data Feed (CDF) enabled, but some delete events arrive out of order due to upstream delays. How does the AUTO CDC API internally ensure deletions are applied correctly despite out-of-order events?

A. It manually sorts incoming events by timestamp before applying changes.
B. It ignores deletions if they arrive after updates for the same key.
C. It uses sequence_by to order events and retains tombstones for deleted rows until older sequences are processed.
D. It runs VACUUM on the target table to purge conflicting records.

Answer: C

Explanation:
AUTO CDC uses the sequence_by column to deterministically order change events for each key.
Delete operations create tombstones that are retained until all earlier sequence values have been processed, ensuring that out-of-order delete events are still applied correctly and consistently in the target table.

NEW QUESTION # 218
A data engineer wants to enforce the principle of least privilege when configuring ACLs for Databricks jobs in a collaborative workspace. Which approach should the data engineer use?

A. Grant CAN RUN permission to everyone and CAN MANAGE to a single admin group.
B. Use only folder-level permissions and avoid setting permissions on individual jobs.
C. Grant all users CAN MANAGE permission on all jobs to avoid access issues.
D. Assign users only the minimum permission level (e.g., CAN RUN or CAN VIEW) required for their role on each job.

Answer: D

Explanation:
Assigning the minimum required permission level on each job ensures users can perform only the actions necessary for their role. This directly enforces the principle of least privilege while maintaining secure and controlled access in a collaborative workspace.

NEW QUESTION # 219
Which REST API call can be used to review the notebooks configured to run as tasks in a multi- task job?

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

Answer: E

Explanation:
https://docs.databricks.com/api/workspace/jobs/getresponses/settings/tasks/notebook_task/noteb ook_path

NEW QUESTION # 220
Incorporating unit tests into a PySpark application requires upfront attention to the design of your jobs, or a potentially significant refactoring of existing code.
Which statement describes a main benefit that offset this additional effort?

A. Yields faster deployment and execution times
B. Validates a complete use case of your application
C. Improves the quality of your data
D. Troubleshooting is easier since all steps are isolated and tested individually
E. Ensures that all steps interact correctly to achieve the desired end result

Answer: D

Explanation:
Unit tests are small, isolated tests that are used to check specific parts of the code, such as functions or classes.

NEW QUESTION # 221
A transactions table has been liquid clustered on the columns product_id, user_id, and event_date. Which operation lacks support for cluster on write?

A. spark.write.format('delta').mode('append')
B. CTAS and RTAS statements
C. INSERT INTO operations
D. spark.writestream.format('delta').mode('append')

Answer: D

Explanation:
Delta Lake's Liquid Clustering is an advanced feature that improves query performance by dynamically clustering data without requiring costly compaction steps like traditional Z-ordering.
When performing writes to a Liquid Clustered table, some write operations automatically maintain clustering, while others do not.

NEW QUESTION # 222
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