Databricks Certified Data Engineer Professional Exam Prep

This section covers building data-processing code in Python and SQL for the Databricks Lakehouse Platform. It emphasizes scalable project structure, dependency management, UDFs, ETL pipeline development, orchestration, environment configuration, and testing for production-grade data engineering solutions.

• Using Python and Tools for development
• Design and implement a scalable Python project structure optimized for Databricks Asset Bundles (DABs), enabling modular development, deployment automation, and CI/CD integration
• Manage third-party library installations
• Develop User-Defined Functions
• Building and Testing an ETL pipeline with Lakeflow Declarative Pipelines, SQL, and Apache Spark on the Databricks platform
• Build and manage reliable, production-ready data pipelines
• Create and Automate ETL workloads using Jobs via UI/APIs/CLI

Key references: DE Professional official exam guide · ExamPal shared topic tree

Covers designing and implementing data ingestion pipelines for efficiently ingesting a variety of data formats from diverse sources. It also includes building append-only pipelines that can handle both batch and streaming data using Delta.

• 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

Key references: DE Professional official exam guide · ExamPal shared topic tree

Covers advanced data transformation, cleansing, and quality practices for working with large datasets. The section emphasizes efficient Spark SQL and PySpark implementations, including window functions, joins, and aggregations, as well as processes for isolating bad data using Lakeflow Declarative Pipelines or autoloader in classic jobs.

• Write efficient Spark SQL and PySpark code to apply advanced data transformations, including window functions, joins, and aggregations, to manipulate and analyze large Datasets
• Use window functions
• Use joins
• Use aggregations
• Develop a quarantining process for bad data with Lakeflow Declarative Pipelines or autoloader in classic jobs
• Quarantine bad data

Key references: DE Professional official exam guide · ExamPal shared topic tree

This section covers secure data sharing between Databricks deployments and with external platforms, as well as federation across supported source systems. It emphasizes Delta Sharing, Databricks-to-Databricks sharing, open sharing protocols, and Lakehouse Federation governance.

• Demonstrate delta sharing securely between Databricks deployments using Databricks to Databricks Sharing(D2D) or to external platforms using open sharing protocol(D2O)
• Secure sharing between Databricks deployments
• Configure Lakehouse Federation with proper governance across supported source Systems
• Lakehouse Federation governance
• Use Delta Share to share live data from Lakehouse to any computing platform
• Share live Lakehouse data

Key references: DE Professional official exam guide · ExamPal shared topic tree

This section covers observability and alerting practices for Databricks workloads, including how to monitor resource utilization, cost, auditing, and workload performance. It also covers the tools and interfaces used to create alerts for data quality and job or pipeline issues.

• Monitoring
• Use system tables for observability
• Use Query Profiler UI and Spark UI
• Use the Databricks REST APIs/Databricks CLI
• Use Lakeflow Declarative Pipelines Event Logs
• Alerting
• Use SQL Alerts to monitor data quality

Key references: DE Professional official exam guide · ExamPal shared topic tree

Covers techniques for reducing operational overhead and improving query performance in Databricks and Unity Catalog environments. The section emphasizes managed tables, Delta optimization features, query execution tuning, and the use of query profiles to diagnose bottlenecks on large datasets.

• Understand how / why using Unity Catalog managed tables reduces operation Overhead and maintenance burden
• Managed tables reduce overhead
• Understand delta optimization techniques, such as deletion vectors and liquid clustering.​ ​
• Deletion vectors and liquid clustering
• Understand the optimization techniques used by Databricks to ensure the performance of queries on large datasets (data skipping, file pruning, etc)
• Data skipping and file pruning
• Apply Change Data Feed (CDF) to address specific limitations of streaming tables and enhance latency

Key references: DE Professional official exam guide · ExamPal shared topic tree

Covers security controls and compliance practices for protecting workspace objects and sensitive table data. The section includes access control, row and column-level protection, anonymization techniques, PII masking, and data retention/purging requirements.

• Applying Data Security mechanisms
• Data security mechanisms
• Use ACLs to secure Workspace Objects, enforcing the principle of least privilege, including enforcing principles like least privilege, policy enforcement
• ACLs for workspace objects
• Use row filters and column masks to filter and mask sensitive table data
• Row filters and column masks
• Apply anonymization and pseudonymization methods such as Hashing, Tokenization, Suppression, and Generalization to confidential data

Key references: DE Professional official exam guide · ExamPal shared topic tree

Covers the governance of enterprise data, including how metadata and descriptions improve discoverability and how permissions are inherited in Unity Catalog. The section focuses on making data easier to find and on understanding access control behavior within the catalog.

• Create and add descriptions/metadata about enterprise data to make it more discoverable
• Demonstrate understanding of Unity Catalog permission inheritance model

Key references: DE Professional official exam guide · ExamPal shared topic tree

This section covers troubleshooting failed jobs and pipelines using Databricks diagnostic tools, then deploying Databricks resources through CI/CD workflows. It includes both debugging operational issues and implementing deployment automation with Databricks-native tooling.

• 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
• Use Lakeflow Declarative Pipelines event logs & the Spark UI to debug Lakeflow Declarative Pipelines and Spark pipelines
• Deploying CI/CD
• Build and Deploy Databricks resources using Databricks Asset Bundles
• Configure and integrate with Git-based CI/CD workflows using Databricks Git Folders for notebook and code deployment

Key references: DE Professional official exam guide · ExamPal shared topic tree

Covers designing and implementing scalable data models using Delta Lake for large datasets. It also includes data layout optimization with Liquid Clustering, comparing it to Partitioning and ZOrder, and designing dimensional models for analytical workloads.

• Design and implement scalable data models using Delta Lake to manage large datasets
• Simplify data layout decisions and optimize query performance using Liquid Clustering
• Identify the benefits of using liquid Clustering over Partitioning and ZOrder
• Design Dimensional Models for analytical workloads, ensuring efficient querying and aggregation

Key references: DE Professional official exam guide · ExamPal shared topic tree