Microsoft DP-203 (Data Engineering on Microsoft Azure) Exam is a certification exam designed to test the skills and knowledge of data engineers who work with Microsoft Azure. DP-203 exam is intended for professionals who work with data engineering and are interested in using Azure technologies to build and manage data solutions. The DP-203 exam is designed to assess the candidate's ability to design, implement, and maintain data pipelines, data storage, data processing, and data security in Azure.
Concise contents
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A true simulation environment
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One of the key benefits of the DP-203 exam is that it is recognized by Microsoft as a valid certification for data engineers who work with Azure. This means that individuals who pass the exam can demonstrate to potential employers that they have the skills and knowledge needed to be successful in this role. Additionally, Microsoft Azure is one of the most widely used cloud platforms in the world, so having a certification in this area can open up new career opportunities.
Reference: https://docs.microsoft.com/en-us/learn/certifications/exams/dp-203
A brief introduction to the course
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Microsoft DP-203 Exam Syllabus Topics:
| Section | Weight | Objectives |
| Topic 1: Design and develop data processing | 25–30% | - Develop data processing solutions
- 1. Manage data flows and dependencies
- 2. Transform data using Spark, SQL, or other tools
- 3. Implement data ingestion
- 4. Implement incremental processing
- Design data processing pipelines
- 1. Design for data integration and transformation
- 2. Design batch processing solutions
- 3. Design orchestration and scheduling
- 4. Design real-time processing solutions
|
| Topic 2: Design and implement data security | 10–15% | - Implement authentication and authorization
- 1. Manage access to data resources
- 2. Configure identity and access management
- Protect data at rest and in transit
- 1. Manage data privacy and compliance
- 2. Configure data masking and redaction
- 3. Implement encryption
|
| Topic 3: Design and implement data storage | 40–45% | - Design data storage structures
- 1. Design for data durability and availability
- 2. Design for scalability and performance
- 3. Recommend storage solutions for analytical workloads
- 4. Design partitioning strategies
- Implement data storage
- 1. Implement data lake storage
- 2. Create and configure data storage accounts
- 3. Manage data lifecycle and retention policies
- 4. Implement relational and non-relational data stores
|
| Topic 4: Monitor and optimize data storage and data processing | 10–15% | - Monitor data solutions
- 1. Monitor storage performance and health
- 2. Monitor pipeline execution and errors
- 3. Log and analyze activity and usage
- Optimize performance and cost
- 1. Tune pipelines and workloads
- 2. Optimize storage and compute resources
- 3. Improve query and processing efficiency
|