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

Section	Objectives
Topic 1: Monitoring and Alerting	- 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 - Monitoring 1. Use Query Profile and Spark UI to monitor workloads 2. Use system tables for observability of resource utilization, cost, auditing, and workloads 3. Use Lakeflow Declarative Pipelines event logs to monitor pipelines 4. Use Databricks REST APIs and Databricks CLI to monitor jobs and pipelines
Topic 2: Debugging and Deploying	- Deploying CI/CD 1. Configure and integrate Git-based CI/CD workflows using Databricks Git folders for notebook and code deployment 2. Build and deploy Databricks resources using Databricks Asset Bundles - 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
Topic 3: 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
Topic 4: 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. Use ACLs to secure workspace objects and enforce the principle of least privilege 3. Apply anonymization and pseudonymization methods including hashing, tokenization, suppression, and generalization
Topic 5: Developing Code for Data Processing using Python and SQL	- Using Python and Tools for Development 1. Develop User-Defined Functions using Pandas/Python UDF 2. Manage and troubleshoot external third-party library installations and dependencies, including PyPI packages, local wheels, and source archives 3. Design and implement a scalable Python project structure optimized for Databricks Asset Bundles, enabling modular development, deployment automation, and CI/CD integration - Building and Testing an ETL Pipeline with Lakeflow Declarative Pipelines, SQL, and Apache Spark 1. Create pipeline components using control flow operators such as if/else and foreach 2. Explain the advantages and disadvantages of streaming tables compared to materialized views 3. Use APPLY CHANGES APIs to simplify CDC in Lakeflow Declarative Pipelines 4. Build and manage reliable, production-ready batch and streaming data pipelines using Lakeflow Declarative Pipelines and Auto Loader 5. Develop unit and integration tests using assertDataFrameEqual, assertSchemaEqual, DataFrame.transform, testing frameworks, and debugging tools 6. Choose appropriate configurations for environments, dependencies, high-memory notebook tasks, and retry behavior 7. Create and automate ETL workloads using Jobs through the UI, APIs, or CLI 8. Compare Spark Structured Streaming and Lakeflow Declarative Pipelines to determine the optimal approach for scalable ETL pipelines
Topic 6: 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
Topic 7: Cost & Performance Optimization	- Optimize cost and performance 1. Understand how and why Unity Catalog managed tables reduce operational overhead and maintenance burden 2. Understand Databricks query optimization techniques for large datasets, including data skipping and file pruning 3. Understand Delta optimization techniques such as deletion vectors and liquid clustering 4. Apply Change Data Feed to address streaming table limitations and improve latency 5. Use query profiling to identify bottlenecks such as inefficient joins and data shuffling
Topic 8: 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
Topic 9: 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. Use Delta Sharing to share live data from the Lakehouse with any computing platform 3. Configure Lakehouse Federation with appropriate governance across supported source systems
Topic 10: 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

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Databricks Certified Data Engineer Professional Sample Questions (Q204-Q209):

NEW QUESTION # 204
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. ALL PRIVILEGES on the finance_data catalog.
B. Make the finance team lead a metastore admin.
C. MANAGE privilege on the finance_data catalog.
D. GRANT OPTION privilege on the finance_data catalog.

Answer: C

Explanation:
The MANAGE privilege in Unity Catalog provides the ability to grant and revoke privileges on the specified object (in this case, a catalog) without giving full administrative access or ownership.
This is the Databricks-recommended approach for delegating governance responsibilities while preserving the principle of least privilege.
By contrast, the ALL PRIVILEGES option grants excessive access (including read and write permissions), and metastore admin status provides global control over all catalogs--far exceeding the requirement. The MANAGE privilege enables the finance team lead to control access to objects within finance_data responsibly while limiting overall administrative exposure.

NEW QUESTION # 205
Which approach demonstrates a modular and testable way to use DataFrame transform for ETL code in PySpark?

Answer: C

Explanation:
Using DataFrame.transform with a pure transformation function promotes modular, reusable, and easily testable ETL logic. Each transformation is encapsulated as a standalone function, can be independently unit tested, and composed cleanly in a pipeline without coupling to orchestration or class state.

NEW QUESTION # 206
A data engineer is designing a secure data sharing strategy for their organization. The company needs to share sensitive customer analytics data with two different partners. Partner A uses Databricks with Unity Catalog enabled, while Partner B uses Apache Spark on AWS without Databricks. How should the company implement secure data sharing for these scenarios?

A. For Partner A, implement Databricks-to-Databricks sharing (D2D) with Unit Catalog integration and no-token exchange system. For Partner B, use open sharing protocol (D2O) with either bearer tokens or OIDC federation for authentication, ensuring both approaches maintain robust security and governance.
B. Open sharing protocol (D2O) should be used for both partners because it provides better security than D2D sharing. The bearer token approach is always more secure than Unity Catalog's native authentication.
C. Both partners should use the same Delta Sharing approach since security requirements are identical. You should create bearer tokens for both partners and use the open sharing protocol (D2O) for maximum compatibility.
D. Databricks-to-Databricks sharing (D2D) can only be used within the same cloud provider, so you must use open sharing (D2O) for any cross-cloud scenarios. Unit Catalog governance is not available when sharing with external platforms.

Answer: A

Explanation:
Databricks-to-Databricks sharing with Unity Catalog provides the most seamless and secure option for Partner A by enabling native governance, fine-grained access controls, and a no-token exchange model. For Partner B, which does not use Databricks, the open sharing protocol enables secure access from external Spark environments using standard authentication mechanisms such as bearer tokens or OIDC federation, while still enforcing sharing policies and protecting sensitive data.

NEW QUESTION # 207
A data engineering team needs to implement a tagging system for their tables as part of an automated ETL process, and needs to apply tags programmatically to tables in Unity Catalog.
Which SQL command adds tags to a table programmatically?

A. APPLY TAGS ON table_name VALUES ('key1' = 'value1', 'key2' = 'value2');
B. SET TAGS FOR table_name AS ('key1' = 'value1', 'key2' = 'value2');
C. COMMENT ON TABLE table_name TAGS ('key1' = 'value1', 'key2' = 'value2');
D. ALTER TABLE table_name SET TAGS ('key1' = 'value1', 'key2' = 'value2');

Answer: D

Explanation:
Unity Catalog supports programmatic tagging through the ALTER TABLE statement. Using SET TAGS allows tags to be added or updated directly on a table as part of automated ETL workflows, making it the correct and supported SQL command for managing table tags.

NEW QUESTION # 208
Where in the Spark UI can one diagnose a performance problem induced by not leveraging predicate push-down?

A. In the Stage's Detail screen, in the Completed Stages table, by noting the size of data read from the Input column
B. In the Storage Detail screen, by noting which RDDs are not stored on disk
C. In the Delta Lake transaction log. by noting the column statistics
D. In the Query Detail screen, by interpreting the Physical Plan
E. In the Executor's log file, by gripping for "predicate push-down"

Answer: D

Explanation:
This is the correct answer because it is where in the Spark UI one can diagnose a performance problem induced by not leveraging predicate push-down. Predicate push-down is an optimization technique that allows filtering data at the source before loading it into memory or processing it further. This can improve performance and reduce I/O costs by avoiding reading unnecessary data. To leverage predicate push-down, one should use supported data sources and formats, such as Delta Lake, Parquet, or JDBC, and use filter expressions that can be pushed down to the source. To diagnose a performance problem induced by not leveraging predicate push-down, one can use the Spark UI to access the Query Detail screen, which shows information about a SQL query executed on a Spark cluster. The Query Detail screen includes the Physical Plan, which is the actual plan executed by Spark to perform the query. The Physical Plan shows the physical operators used by Spark, such as Scan, Filter, Project, or Aggregate, and their input and output statistics, such as rows and bytes. By interpreting the Physical Plan, one can see if the filter expressions are pushed down to the source or not, and how much data is read or processed by each operator.

NEW QUESTION # 209
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