Exam Prep – Databricks Certified Data Engineer Professional
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Learning Track
Exam Prep
Delivery methods
On-Site, Virtual
Duration
1 day
This course prepares learners for the Databricks Certified Data Engineer Professional exam using a question-driven approach. Each module begins with realistic exam-style practice questions based on the official exam domains. Instructors then break down every answer option—explaining why it is correct or incorrect—and weave in targeted teaching on Databricks Lakehouse best practices. By course end, learners will be exam-ready and confident applying their knowledge to production-grade solutions.
Learning Objectives
By the end of this course, learners will be able to:
- Confidently answer questions across all ten exam domains.
- Understand the reasoning behind both correct and incorrect options.
- Apply advanced Databricks skills: Lakeflow Declarative Pipelines, Delta Lake optimizations, Unity Catalog, security and compliance, monitoring, and CI/CD deployments.
- Demonstrate readiness to deliver production-grade, secure, and optimized data engineering solutions.
Audience
- Candidates preparing for the Databricks Certified Data Engineer Professional exam (post-September 30, 2025)
- Experienced data engineers with 1+ year hands-on with the Databricks Lakehouse
- Professionals aiming to validate advanced data engineering skills for production workloads
Prerequisites
- Strong SQL and Python proficiency
- Hands-on experience with Databricks pipelines, Spark, Unity Catalog, and orchestration
- Familiarity with data governance, CI/CD workflows, and troubleshooting in Databricks
Course outline
- Certification format, domains, scoring, timing
- Strategies for analyzing multiple-choice questions
- Question types and common distractors
- Pacing strategies and flagging questions for review
- Designing scalable Python project structures with Asset Bundles (DABs)
- Managing third-party library installations (PyPI, wheels, source archives)
- Writing and optimizing UDFs (Pandas/Python UDFs)
- Building and testing ETL pipelines with Lakeflow Declarative Pipelines, SQL, and Spark
- Automating ETL workloads using Jobs via UI, APIs, and CLI
- Streaming tables vs. materialized views, APPLY CHANGES APIs, and control flow operators
- Unit and integration testing strategies for pipelines
- Designing ingestion pipelines for diverse formats (Delta, Parquet, ORC, Avro, JSON, CSV, XML, text, binary)
- Ingesting from diverse sources such as message buses and cloud storage
- Building append-only pipelines for batch and streaming data with Delta
- Writing efficient Spark SQL and PySpark transformations (window functions, joins, aggregations)
- Quarantining bad data with Lakeflow or Auto Loader
- Configuring Delta Sharing (Databricks-to-Databricks and open protocol)
- Setting up Lakehouse Federation with proper governance
- Sharing live data securely across platforms
- Using system tables for observability (utilization, cost, auditing, monitoring)
- Monitoring workloads with Query Profiler UI and Spark UI
- Monitoring jobs and pipelines with REST APIs/CLI
- Using Lakeflow Event Logs for pipeline observability
- Setting up SQL Alerts and job performance notifications
- Benefits of Unity Catalog managed tables for reducing overhead
- Delta optimizations: Deletion vectors, liquid clustering
- Databricks query optimizations: Data skipping, file pruning, join strategies
- Using Change Data Feed (CDF) for latency and streaming limitations
- Diagnosing bottlenecks with Query Profiler
- Applying ACLs and enforcing least-privilege access
- Using row filters and column masks for sensitive data
- Anonymization and pseudonymization (hashing, tokenization, suppression, generalization)
- Building compliant pipelines with PII masking
- Designing purging solutions for retention policy compliance
- Adding metadata and descriptions for discoverability
- Understanding Unity Catalog’s permission inheritance model
- Using Spark UI, cluster logs, system tables, and query profiles for debugging
- Remediating failed jobs with repairs and parameter overrides
- Debugging Lakeflow pipelines with event logs
- Deploying with Asset Bundles and Git-based CI/CD workflows
- Designing scalable models in Delta Lake for large datasets
- Simplifying layouts and optimizing queries with Liquid Clustering
- Comparing liquid clustering, partitioning, and Z-Order
- Designing dimensional models for analytical workloads
