Databricks Data Engineer Professional Test Series

About Databricks Data Engineer Professional Test Series

These practice tests are designed to help you understand all of the exam syllabus for Databricks Certified Data Engineer Professional so you can pass the exam but also help you learn the concepts of the Databricks platform.The questions in each of the practice tests are on par with the actual exam questions you will be facing during the exam.I can assure you that you can pass with more than 90% if you understand the questions well. This is purely questions oriented syllabus covering tests,You have to go through the explanations after each attempt so that you will get the concepts and will be ready to face any twisted question from the topic. note: Go through Databricks partner academy course first and attempt this test for maximum efficiency of utilization of the course. Brief description on the exam: The Databricks Certified Data Engineer Professional certification exam assesses an individual’s ability to use Databricks to perform advanced data engineering tasks. This includes an understanding of the Databricks platform and developer tools like Apache Spark, Delta Lake, MLflow, and the Databricks CLI and REST API. It also assesses the ability to build optimized and cleaned ETL pipelines. Additionally, the ability to model data into a lakehouse using knowledge of general data modeling concepts will be assessed. Finally, being able to ensure that data pipelines are secure, reliable, monitored and tested before deployment will also be included in this exam. Individuals who pass this certification exam can be expected to complete advanced data engineering tasks using Databricks and its associated tools. Duration Exam participants will have 120 minutes to complete each practice exam with 45 multiple choice questions. Questions There are 60 multiple-choice questions on each practice exam. The questions will be distributed by high-level topic in the following way: 1. Databricks Tooling – 20% 2. Data Processing – 30% 3. Data Modeling – 20% 4. Security and Governance – 10% 5. Monitoring and Logging – 10% 6. Testing and Deployment – 10%