Handsome Savings - Limited Time Offer 30% OFF - Ends In 0d 0h 0m 0s Coupon code: 50OFF
Trusted Worldwide Questions & Answers
Pass your Databricks-Certified-Professional-Data-Engineer Exam with accurate Questions & Answers
Databricks Certified Data Engineer Professional
Last Updated: Oct 3, 2024
120
120 Questions and Answers for the Databricks Databricks-Certified-Professional-Data-Engineer exam
493
Students Passed the "Databricks Databricks-Certified-Professional-Data-Engineer" exam
94.6%
Average score during Real Exams at the Testing Centre
Databricks Certified Data Engineer Professional Syllabus
- Databricks Tooling: The Databricks Tooling topic encompasses the various features and functionalities of Delta Lake. This includes understanding the transaction log, Optimistic Concurrency Control, Delta clone, indexing optimizations, and strategies for partitioning data for optimal performance in the Databricks SQL service.
- Data Processing: The topic covers understanding partition hints, partitioning data effectively, controlling part-file sizes, updating records, leveraging Structured Streaming and Delta Lake, implementing stream-static joins and deduplication. Additionally, it delves into utilizing Change Data Capture, and addressing performance issues related to small files.
- Data Modeling: It focuses on understanding the objectives of data transformations, using Change Data Feed, applying Delta Lake cloning, designing multiplex bronze tables. Lastly it discusses implementing incremental processing and data quality enforcement, implementing lookup tables, and implementing Slowly Changing Dimension tables, and implementing SCD Type 0, 1, and 2 tables.
- Security & Governance: It discusses creating Dynamic views to accomplishing data masking and using dynamic views to control access to rows and columns.
- Monitoring & Logging: This topic includes understanding the Spark UI, inspecting event timelines and metrics, drawing conclusions from various UIs, designing systems to control cost and latency SLAs for production streaming jobs, and deploying and monitoring both streaming and batch jobs.
- Testing & Deployment: It discusses adapting notebook dependencies to use Python file dependencies, leveraging Wheels for imports, repairing and rerunning failed jobs, creating jobs based on common use cases, designing systems to control cost and latency SLAs, configuring the Databricks CLI, and using the REST API to clone a job, trigger a run, and export the run output.