Use this quick start guide to collect all the information about Databricks Data Engineer Professional Certification exam. This study guide provides a list of objectives and resources that will help you prepare for items on the Databricks Certified Data Engineer Professional (Data Engineer Professional) exam. The Sample Questions will help you identify the type and difficulty level of the questions and the Practice Exams will make you familiar with the format and environment of an exam. You should refer this guide carefully before attempting your actual Databricks Certified Data Engineer Professional certification exam.
The Databricks Data Engineer Professional certification is mainly targeted to those candidates who want to build their career in Data Engineer domain. The Databricks Certified Data Engineer Professional exam verifies that the candidate possesses the fundamental knowledge and proven skills in the area of Databricks Lakehouse Data Engineer Professional.
Databricks Data Engineer Professional Exam Summary:
| Exam Name | Databricks Certified Data Engineer Professional |
| Exam Code | Data Engineer Professional |
| Exam Price | $200 (USD) |
| Duration | 120 mins |
| Number of Questions | 60 |
| Passing Score | 70% |
| Schedule Exam | Kryterion Webassesor |
| Sample Questions | Databricks Data Engineer Professional Sample Questions |
| Practice Exam | Databricks Data Engineer Professional Certification Practice Exam |
Databricks Lakehouse Data Engineer Professional Exam Syllabus Topics:
| Topic | Details | Weights |
|---|---|---|
| Databricks Tooling |
- Explain how Delta Lake uses the transaction log and cloud object storage to guarantee atomicity and durability - Describe how Delta Lake’s Optimistic Concurrency Control provides isolation, and which transactions might conflict - Describe basic functionality of Delta clone. - Apply common Delta Lake indexing optimizations including partitioning, zorder, bloom filters, and file sizes - Implement Delta tables optimized for Databricks SQL service - Contrast different strategies for partitioning data (e.g. identify proper partitioning columns to use) |
20% |
| Data Processing (Batch processing, Incremental processing, and Optimization) |
- Describe and distinguish partition hints: coalesce, repartition, repartition by range, and rebalance - Contrast different strategies for partitioning data (e.g. identify proper partitioning columns to use) - Articulate how to write Pyspark dataframes to disk while manually controlling the size of individual part-files. - Articulate multiple strategies for updating 1+ records in a spark table (Type 1) - Implement common design patterns unlocked by Structured Streaming and Delta Lake. - Explore and tune state information using stream-static joins and Delta Lake - Implement stream-static joins - Implement necessary logic for deduplication using Spark Structured Streaming - Enable CDF on Delta Lake tables and re-design data processing steps to process CDC output instead of incremental feed from normal Structured Streaming read - Leverage CDF to easily propagate deletes - Demonstrate how proper partitioning of data allows for simple archiving or deletion of data - Articulate, how “smalls” (tiny files, scanning overhead, over partitioning, etc) induce performance problems into Spark queries |
30% |
| Data Modeling |
- Describe the objective of data transformations during promotion from bronze to silver - Discuss how Change Data Feed (CDF) addresses past difficulties propagating updates and deletes within Lakehouse architecture - Apply Delta Lake clone to learn how shallow and deep clone interact with source/target tables. - Design a multiplex bronze table to avoid common pitfalls when trying to productionalize streaming workloads. - Implement best practices when streaming data from multiplex bronze tables. - Apply incremental processing, quality enforcement, and deduplication to process data from bronze to silver - Make informed decisions about how to enforce data quality based on strengths and limitations of various approaches in Delta Lake - Implement tables avoiding issues caused by lack of foreign key constraints - Add constraints to Delta Lake tables to prevent bad data from being written - Implement lookup tables and describe the trade-offs for normalized data models - Diagram architectures and operations necessary to implement various Slowly Changing Dimension tables using Delta Lake with streaming and batch workloads. - Implement SCD Type 0, 1, and 2 tables |
20% |
| Security & Governance |
- Create Dynamic views to perform data masking - Use dynamic views to control access to rows and columns |
10% |
| Monitoring & Logging |
- Describe the elements in the Spark UI to aid in performance analysis, application debugging, and tuning of Spark applications. - Inspect event timelines and metrics for stages and jobs performed on a cluster - Draw conclusions from information presented in the Spark UI, Ganglia UI, and the Cluster UI to assess performance problems and debug failing applications. - Design systems that control for cost and latency SLAs for production streaming jobs. - Deploy and monitor streaming and batch jobs |
10% |
| Testing & Deployment |
- Adapt a notebook dependency pattern to use Python file dependencies - Adapt Python code maintained as Wheels to direct imports using relative paths - Repair and rerun failed jobs - Create Jobs based on common use cases and patterns - Create a multi-task job with multiple dependencies - Design systems that control for cost and latency SLAs for production streaming jobs. - Configure the Databricks CLI and execute basic commands to interact with the workspace and clusters. - Execute commands from the CLI to deploy and monitor Databricks jobs. - Use REST API to clone a job, trigger a run, and export the run output |
10% |
To ensure success in Databricks Lakehouse Data Engineer Professional certification exam, we recommend authorized training course, practice test and hands-on experience to prepare for Databricks Certified Data Engineer Professional (Data Engineer Professional) exam.