Databricks Certified Data Engineer Professional Preparation Details
The Databricks Certified Data Engineer Professional exam validates advanced skills in building, optimizing, and securing production-grade ETL pipelines on the Databricks Data Intelligence Platform. This guide breaks down every domain, task, and objective from the official exam guide, pairing each with verified Databricks documentation. You can also explore more Databricks certification study guides on the Databricks category to keep building your skills.
Databricks Certified Data Engineer Professional Materials
| Coursera | Data Engineering with Delta Lake on Databricks |
| Udemy | Databricks Certified Data Engineer Professional |
| Whizlabs | Databricks Certified Data Engineer Professional |
Section 1: Developing Code for Data Processing using Python and SQL (22% of scored content)
Using Python and Tools for development
Design and implement a scalable Python project structure optimized for Declarative Automation Bundles (formerly Databricks Asset Bundles / DABs), enabling modular development, deployment automation, and CI/CD integration.
What are Declarative Automation Bundles?
Declarative Automation Bundles project templates
Declarative Automation Bundles configuration
Bundle configuration in Python
Manage and troubleshoot external third-party library installations and dependencies in Databricks, including PyPI packages, local wheels, and source archives.
Notebook-scoped Python libraries
Restart the Python process on Databricks
Develop User-Defined Functions (UDFs) using Pandas/Python UDF.
What are user-defined functions (UDFs)?
User-defined scalar functions – Python
Building and Testing an ETL pipeline with Lakeflow Spark Declarative Pipelines, SQL, and Apache Spark on the Databricks Platform
Build and manage reliable, production-ready data pipelines for batch and streaming data using Lakeflow Spark Declarative Pipelines and Autoloader.
Lakeflow Spark Declarative Pipelines
Configure Auto Loader for production workloads
Best practices for Lakeflow pipelines
Create and Automate ETL workloads using Jobs via UI/APIs/CLI.
Configure and edit Lakeflow Jobs
Automate job creation and management
Explain the advantages and disadvantages of streaming tables compared to materialized views.
Tables and views in Databricks
Use AUTO CDC APIs (formerly APPLY CHANGES) to simplify CDC in Lakeflow Spark Declarative Pipelines.
The AUTO CDC APIs: Simplify change data capture with pipelines
Change data capture and snapshots
Compare Spark Structured Streaming and Lakeflow Spark Declarative Pipelines to determine the optimal approach for building scalable ETL pipelines.
Lakeflow Spark Declarative Pipelines
Create a pipeline component that uses control flow operators (e.g., if/else, for/each, etc.).
Control the flow of tasks within Lakeflow Jobs
Add branching logic to a job with the If/else task
Use a For each task to run another task in a loop
Choose the appropriate configs for environments and dependencies, high memory for notebook tasks, and auto-optimization to disallow retries.
Run your Lakeflow Jobs with serverless compute for workflows
Configure the serverless environment
Classic compute configuration best practices
Develop unit and integration tests using assertDataFrameEqual, assertSchemaEqual, DataFrame.transform, and testing frameworks, to ensure code correctness, including a built-in debugger.
Simplify PySpark testing with DataFrame equality functions
pyspark.testing.assertDataFrameEqual
pyspark.testing.assertSchemaEqual
Section 2: Data Ingestion & Acquisition (7% of scored content)
Topics Covered
Design and implement data ingestion pipelines to efficiently ingest a variety of data formats including Delta Lake, Parquet, ORC, AVRO, JSON, CSV, XML, Text and Binary from diverse sources such as message buses and cloud storage.
Create an append-only data pipeline capable of handling both batch and streaming data using Delta.
What is Delta Lake in Databricks?
Tables and views in Databricks
Load and process data incrementally with Lakeflow Spark Declarative Pipelines
Section 3: Data Transformation, Cleansing, and Quality (10% of scored content)
Topics Covered
Write efficient Spark SQL and PySpark code to apply advanced data transformations, including window functions, joins, and aggregations, to manipulate and analyze large Datasets.
Develop a quarantining process for bad data with Lakeflow Spark Declarative Pipelines, or autoloader in classic jobs.
Manage data quality with pipeline expectations
Expectation recommendations and advanced patterns
Best practices for reliability
Section 4: Data Sharing and Federation (5% of scored content)
Topics Covered
Demonstrate delta sharing securely between Databricks deployments using Databricks to Databricks Sharing (D2D) or to external platforms using the open sharing protocol (D2O).
What is the OpenSharing Databricks-to-Databricks protocol?
What is the OpenSharing Databricks-to-Open sharing protocol?
Configure Lakehouse Federation with proper governance across the supported source Systems.
Connect to external databases and catalogs
Use Delta Share to share live data from Lakehouse to any computing platform.
Read data shared using Databricks-to-Databricks Delta Sharing (for recipients)
Section 5: Monitoring and Alerting (10% of scored content)
Monitoring
Use system tables for observability over resource utilization, cost, auditing and workload monitoring.
Audit log system table reference
Phase 9: Design observability strategy
Use Query Profiler UI and Spark UI to monitor workloads.
Use the Databricks REST APIs/Databricks CLI for monitoring jobs and pipelines.
Monitoring and observability for Lakeflow Jobs
Use Lakeflow Spark Declarative Pipelines Event Logs to monitor pipelines.
Lakeflow Declarative Pipelines event log
Alerting
Use SQL Alerts to monitor data quality.
Set up alerts based on anomaly detection
Use the Lakeflow Jobs UI and Jobs API to set up notifications for job status and performance issues.
Manage notification destinations
Section 6: Cost & Performance Optimization (13% of scored content)
Topics Covered
Understand how / why using Unity Catalog managed tables reduces operations overhead and maintenance burden.
Unity Catalog managed tables for Delta Lake and Apache Iceberg
Databricks Unity Catalog table types
How Unity Catalog Managed Tables Automate Performance at Scale
Overhead and maintenance burden.
Unity Catalog managed tables for Delta Lake and Apache Iceberg
Databricks Unity Catalog table types
How Unity Catalog Managed Tables Automate Performance at Scale
Understand delta optimization techniques, such as deletion vectors and liquid clustering.
Deletion vectors in Databricks
Use liquid clustering for tables
Understand the optimization techniques used by Databricks to ensure the performance of queries on large datasets (data skipping, file pruning, etc.).
Apply Change Data Feed (CDF) to address specific limitations of streaming tables and enhance latency.
Use Delta Lake change data feed on Databricks
Use the query profile to analyze the query and identify bottlenecks, such as bad data skipping, inefficient types of joins, and data shuffling.
Section 7: Ensuring Data Security and Compliance (10% of scored content)
Applying Data Security mechanisms.
Use ACLs to secure Workspace Objects, enforcing the principle of least privilege, including enforcing principles like least privilege, policy enforcement.
Authentication and access control
Use row filters and column masks to filter and mask sensitive table data.
Manually apply row filters and column masks
Apply anonymization and pseudonymization methods, such as Hashing, Tokenization, Suppression, and generalization, to confidential data.
Ensuring Compliance
Implement a compliant batch & streaming pipeline that detects and applies masking of PII to ensure data privacy.
Prepare your data for GDPR compliance
Develop a data purging solution ensuring compliance with data retention policies.
Prepare your data for GDPR compliance
Section 8: Data Governance (7% of scored content)
Topics Covered
Create and add descriptions/metadata about enterprise data to make it more discoverable.
Add comments to data and AI assets
Add AI-generated comments to Unity Catalog objects
Apply tags to Unity Catalog securable objects
Enrich tables with custom metadata
Demonstrate understanding of Unity Catalog permission inheritance model.
Unity Catalog permissions model concepts
Access control in Unity Catalog
Section 9: Debugging and Deploying (10% of scored content)
Debugging and Troubleshooting
Identify pertinent diagnostic information using Spark UI, cluster logs, system tables, and query profiles to troubleshoot errors.
Analyze the errors and remediate the failed job runs with job repairs and parameter overrides.
Troubleshoot and repair job failures
Use Lakeflow Spark Declarative Pipelines event logs and the Spark UI to debug Lakeflow Spark Declarative Pipelines and Spark pipelines.
Lakeflow Declarative Pipelines event log
Deploying CI/CD
Build and deploy Databricks resources using Declarative Automation Bundles (formerly Databricks Asset Bundles)
What are Declarative Automation Bundles?
Configure and integrate with Git-based CI/CD workflows Databricks Git folders (formerly Repos) for notebook and code deployment.
CI/CD with Databricks Git folders
Section 10: Data Modeling (6% of scored content)
Topics Covered
Design and implement scalable data models using Delta Lake to manage large datasets.
What is Delta Lake in Databricks?
Data Warehouse Modeling on Databricks
Simplify data layout decisions and optimize query performance using Liquid Clustering.
Use liquid clustering for tables
Identify the benefits of using liquid Clustering over Partitioning and ZOrder.
When to partition tables on Databricks
Use liquid clustering for tables
Design Dimensional Models for analytical workloads, ensuring efficient querying and aggregation.
Implementing a dimensional data warehouse with Databricks SQL: Part 1
Data Warehouse Modeling on Databricks
Wrapping Up Databricks Certified Data Engineer Professional
This guide covered all ten domains of the Databricks Certified Data Engineer Professional exam, from Lakeflow pipelines and Auto Loader to Unity Catalog governance, security, and dimensional modeling. Working through each objective alongside the linked official documentation is one of the most reliable ways to prepare for this certification. You can also explore more Databricks certification study guides on the Databricks category to keep building your skills. Have a question or tip? Leave a comment below.
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