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

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

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Databricks Certified Data Engineer Professional Sample Questions (Q78-Q83):

NEW QUESTION # 78
What statement is true regarding the retention of job run history?

A. It is retained until you export or delete job run logs
B. It is retained for 60 days, after which logs are archived
C. It is retained for 60 days, during which you can export notebook run results to HTML
D. It is retained for 90 days or until the run-id is re-used through custom run configuration
E. It is retained for 30 days, during which time you can deliver job run logs to DBFS or S3

Answer: C

Explanation:
https://docs.databricks.com/en/workflows/jobs/monitor-job-runs.html

NEW QUESTION # 79
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. Utilize Liquid Clustering (CLUSTER BY AUTO) and Predictive Optimization.
C. Schedule OPTIMIZE/ZORDER to run after each job to improve recent file performance.
D. Enable Delta caching.

Answer: B

Explanation:
According to Databricks Delta Lake optimization documentation, Liquid Clustering is a next- generation file organization capability that automatically manages file co-location without requiring explicit partitioning or manual Z-ORDERing. When combined with Predictive Optimization, Databricks automatically maintains clustering across frequently filtered or queried columns, adapting dynamically as query workloads evolve.
This approach eliminates the need for manual maintenance (such as periodic OPTIMIZE or Z- ORDER commands) while improving query performance on large tables--particularly for high- ingest streaming workloads.
Delta caching (B) only improves performance for cached queries and does not address file layout issues, and (D) handles file size optimization but not clustering. Thus, C is the most efficient, modern, and low-maintenance solution recommended by Databricks.

NEW QUESTION # 80
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. CTAS and RTAS statements
B. spark.writestream.format('delta').mode('append')
C. spark.write.format('delta').mode('append')
D. INSERT INTO operations

Answer: B

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 # 81
A departing platform owner currently holds ownership of multiple catalogs and controls storage credentials and external locations. A data engineer has been asked to ensure continuity: transfer catalog ownership to the platform team group, delegate ongoing privilege management, and retain the ability to receive and share data via Delta Sharing. Which role must be in place to perform these actions across the metastore?

A. Catalog Owner, because catalog owners can transfer any object in any catalog in the metastore.
B. Metastore Admin, because metastore admins can transfer ownership and manage privileges across all metastore objects, including shares and recipients.
C. Workspace Admin, because workspace admins can transfer ownership of any Unity Catalog object.
D. Account Admin, because account admins can only create metastores but cannot change ownership of catalogs.

Answer: B

Explanation:
Metastore Admins have the highest administrative privileges within a Unity Catalog metastore.
They can transfer ownership of any Unity Catalog object, including catalogs, schemas, tables, storage credentials, and external locations. Metastore Admins are also required to manage Delta Sharing configurations such as creating or transferring shares and recipients.
Account Admins, by contrast, only create metastores and cannot change ownership or manage Delta Sharing objects. Workspace Admins have privileges limited to workspace-level management, not cross-metastore access.

NEW QUESTION # 82
A data engineer is working in an interactive notebook with many transformations before outputting the result from display(df.collect() ). The notebook includes wide transformations and a cross join.
The data engineer is getting the following error: "The spark driver has stopped unexpectedly and is restarting. Your notebook will be automatically reattached." Which action should the data engineer take?

A. Look at the compute metrics UI to see if the executors have higher than 90% memory utilization.
B. Check into the Spark UI to see how many jobs are assigned to each stage as they are employing fewer executors.
C. Run the notebook on a single node cluster to keep driver from falling.
D. Rewrite their code to avoid putting memory pressure on the driver node.

Answer: D

Explanation:
Calling df.collect() on a large DataFrame forces all data to be loaded into the driver's memory.
With wide transformations and a cross join, this can easily exceed the driver's capacity, causing it to crash. The data engineer should rewrite the code to avoid collecting large datasets on the driver, using operations like display(df) or writing to storage instead.

NEW QUESTION # 83
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