Overview

Databricks Certified Data Engineer Professional Certification is a highly sought-after credential in the field of data engineering. This certification is designed for individuals who have a strong background in data engineering and are looking to validate their skills and expertise in using Databricks to build and manage data pipelines, conduct data analysis, and optimize data processing workflows.One of the key features of the Databricks Certified Data Engineer Professional Certification is the practice exam. This practice exam is designed to simulate the experience of taking the actual certification exam, allowing candidates to familiarize themselves with the format and types of questions that they may encounter. By taking the practice exam, candidates can assess their readiness for the certification exam and identify areas where they may need to focus their study efforts.This certification exam itself covers a wide range of topics related to data engineering with Databricks, including data ingestion, data transformation, data storage, and data analysis. Candidates will be tested on their ability to design and implement data pipelines using Databricks, optimize data processing workflows for performance and scalability, and troubleshoot common issues that may arise during data processing.To earn the Databricks Certified Data Engineer Professional Certification, candidates must pass the certification exam with a score of 70% or higher. This certification is valid for two years, after which candidates will need to recertify in order to maintain their certification status.In addition to the practice exam, candidates preparing for the Databricks Certified Data Engineer Professional Certification can take advantage of a variety of resources to help them study and prepare for the exam. Databricks offers a range of training courses, study guides, and practice exercises to help candidates build their skills and knowledge in data engineering with Databricks.Databricks Certified Data Engineer Professional Certification is a valuable credential for data engineers who work with Databricks. By earning this certification, candidates can demonstrate their expertise in using Databricks to build and manage data pipelines, conduct data analysis, and optimize data processing workflows. This certification can help data engineers advance their careers, increase their earning potential, and gain recognition for their skills and expertise in the field of data engineering.Databricks Certified Data Engineer Professional Exam Summary:Exam Name: Databricks Certified Data Engineer ProfessionalType: Proctored certificationTotal number of questions: 60Time limit: 120 minutesRegistration fee: $200Question types: Multiple choiceTest aides: None allowedLanguages: English, 日本語, Português BRDelivery method: Online proctoredPrerequisites: None, but related training highly recommendedRecommended experience: 6+ months of hands-on experience performing the data engineering tasks outlined in the exam guideValidity period: 2 yearsDatabricks Certified Data Engineer Professional Exam Syllabus Topics:Databricks Tooling - 20%Data Processing - 30%Data Modeling - 20%Security and Governance - 10%Monitoring and Logging - 10%Testing and Deployment - 10%Databricks ToolingExplain how Delta Lake uses the transaction log and cloud object storage to guarantee atomicity and durabilityDescribe how Delta Lake's Optimistic Concurrency Control provides isolation, and which transactions might conflictDescribe basic functionality of Delta clone.Apply common Delta Lake indexing optimizations including partitioning, zorder, bloom filters, and file sizesImplement Delta tables optimized for Databricks SQL serviceContrast different strategies for partitioning data (e.g. identify proper partitioning columns to use)Data Processing (Batch processing, Incremental processing, and Optimization)Describe and distinguish partition hints: coalesce, repartition, repartition by range, and rebalanceContrast 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 LakeImplement stream-static joinsImplement necessary logic for deduplication using Spark Structured StreamingEnable CDF on Delta Lake tables and re-design data processing steps to process CDC output instead of incremental feed from normal Structured Streaming readLeverage CDF to easily propagate deletesDemonstrate how proper partitioning of data allows for simple archiving or deletion of dataArticulate, how "smalls" (tiny files, scanning overhead, over partitioning, etc) induce performance problems into Spark queriesData ModelingDescribe the objective of data transformations during promotion from bronze to silverDiscuss how Change Data Feed (CDF) addresses past difficulties propagating updates and deletes within Lakehouse architectureApply 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 silverMake informed decisions about how to enforce data quality based on strengths and limitations of various approaches in Delta LakeImplement tables avoiding issues caused by lack of foreign key constraintsAdd constraints to Delta Lake tables to prevent bad data from being writtenImplement lookup tables and describe the trade-offs for normalized data modelsDiagram 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 tablesSecurity & GovernanceCreate Dynamic views to perform data maskingUse dynamic views to control access to rows and columnsMonitoring & LoggingDescribe 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 clusterDraw 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 jobsTesting & DeploymentAdapt a notebook dependency pattern to use Python file dependenciesAdapt Python code maintained as Wheels to direct imports using relative pathsRepair and rerun failed jobsCreate Jobs based on common use cases and patternsCreate a multi-task job with multiple dependenciesDesign 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 outputOverall, the Databricks Certified Data Engineer Professional Certification is a valuable credential for data engineers who work with Databricks. By earning this certification, candidates can demonstrate their expertise in using Databricks to build and manage data pipelines, conduct data analysis, and optimize data processing workflows. This certification can help data engineers advance their careers, increase their earning potential, and gain recognition for their skills and expertise in the field of data engineering.DISCLAIMER: These questions are designed to, give you a feel of the level of questions asked in the actual exam. We are not affiliated with Databricks or Apache. All the screenshots added to the answer explanation are not owned by us. Those are added just for reference to the context.

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