Databricks – Data Engineer ProfessionalThe Databricks Certified Data Engineer Professional certification is designed to validate the skills and knowledge of data engineers who design, build, and optimize reliable and scalable data pipelines on the Databricks Lakehouse Platform. This certification focuses on data engineering best practices, advanced Spark development, data modeling, performance tuning, and workflow orchestration using Databricks.Below is a detailed explanation of the preparation process for this certification, its characteristics, prerequisites, target audience, job importance, and how to perform effective exam simulations with real, updated questions and answers.1. Certification CharacteristicsLevel: Professional / advanced. This certification is intended for data engineers with practical experience in Spark, ETL pipelines, and Databricks platform usage.Exam Duration: Typically 120 minutes.Format: Multiple-choice, multiple-select, and scenario-based questions; taken online.Language: English.Passing Score: Minimum passing score around 70–75% (exact score not publicly disclosed by Databricks).2. PrerequisitesRecommended Experience:At least 1–2 years of experience working with Apache Spark and Databricks.Experience designing, building, and maintaining production ETL pipelines.Familiarity with Databricks notebooks, Delta Lake, and workflow orchestration.Prior Knowledge:Python or Scala for Spark development.Advanced Spark concepts: DataFrames, Spark SQL, caching, partitions, and shuffles.Data modeling for analytics (star/snowflake schema).Basic knowledge of cloud storage (S3, ADLS, GCS) and Databricks
What you'll learn
design and build reliable data pipelines
optimize data processing with Apache Spark
manage ETL workflows on Databricks
perform data modeling for analytics
Course objectives
prepare effectively for the Data Engineer Professional certification exam
understand advanced Spark concepts
familiarize with Databricks notebooks and Delta Lake