Exam DP-203: Data Engineering on Microsoft Azure

Candidates for this exam should have subject matter expertise integrating, transforming, and consolidating data from various structured and unstructured data systems into a structure that is suitable for building analytics solutions.

Azure Data Engineers help stakeholders understand the data through exploration, and they build and maintain secure and compliant data processing pipelines by using different tools and techniques. These professionals use various Azure data services and languages to store and produce cleansed and enhanced datasets for analysis.

Azure Data Engineers also help ensure that data pipelines and data stores are high-performing, efficient, organized, and reliable, given a set of business requirements and constraints. They deal with unanticipated issues swiftly, and they minimize data loss. They also design, implement, monitor, and optimize data platforms to meet the data pipelines needs.

A candidate for this exam must have strong knowledge of data processing languages such as SQL, Python, or Scala, and they need to understand parallel processing and data architecture patterns.

Part of the requirements for: Microsoft Certified: Azure Data Engineer Associate

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Languages: English, Chinese (Simplified), Japanese, Korean

Retirement date: none

This exam measures your ability to accomplish the following technical tasks: design and implement data storage; design and develop data processing; design and implement data security; and monitor and optimize data storage and data processing.

Reference: https://docs.microsoft.com/en-us/learn/certifications/exams/dp-203

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Microsoft DP-203 Exam Syllabus Topics:

Section	Weight	Objectives
Design and Develop Data Processing	25-30%	- Develop batch processing solutions 1. Develop Azure Data Factory pipelines 2. Implement Azure Databricks notebooks 3. Transform data using Apache Spark - Develop stream processing solutions 1. Implement Azure Stream Analytics 2. Process streaming data with Event Hubs
Design and Implement Data Security	10-15%	- Implement data security 1. Implement data encryption 2. Manage sensitive data 3. Configure authentication and authorization
Monitor and Optimize Data Storage and Processing	10-15%	- Monitor data solutions 1. Monitor Spark workloads 2. Monitor pipeline execution - Optimize performance 1. Optimize data partitioning and indexing 2. Optimize query performance
Design and Implement Data Storage	40-45%	- Design a data storage structure 1. Design a lakehouse architecture 2. Design a data warehouse schema 3. Design partitioning strategies - Implement data storage solutions 1. Implement Delta Lake 2. Implement Azure Synapse Analytics storage 3. Implement Azure Data Lake Storage