Skills measured
- Monitor and optimize data storage and data processing (10-15%)
- Design and develop data processing (25-30%)
- Design and implement data security (10-15%)
- Design and implement data storage (40-45%)
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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
Download exam skills outline
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 |
| Topic 1: Design and Implement Data Storage | 40-45% | - Design a data storage structure
- 1. Design a data warehouse schema
- 2. Design a lakehouse architecture
- 3. Design partitioning strategies
- Implement data storage solutions
- 1. Implement Azure Synapse Analytics storage
- 2. Implement Delta Lake
- 3. Implement Azure Data Lake Storage
|
| Topic 2: Monitor and Optimize Data Storage and Processing | 10-15% | - Optimize performance
- 1. Optimize data partitioning and indexing
- 2. Optimize query performance
- Monitor data solutions
- 1. Monitor pipeline execution
- 2. Monitor Spark workloads
|
| Topic 3: Design and Implement Data Security | 10-15% | - Implement data security
- 1. Configure authentication and authorization
- 2. Implement data encryption
- 3. Manage sensitive data
|
| Topic 4: Design and Develop Data Processing | 25-30% | - Develop batch processing solutions
- 1. Implement Azure Databricks notebooks
- 2. Transform data using Apache Spark
- 3. Develop Azure Data Factory pipelines
- Develop stream processing solutions
- 1. Implement Azure Stream Analytics
- 2. Process streaming data with Event Hubs
|