Data Engineering on Microsoft Azure (DP-203) Exam
Data Engineering on Microsoft Azure (DP-203) is designed for individuals with expertise in integrating, transforming, and consolidating data from diverse structured, unstructured, and streaming data systems into an appropriate schema to construct analytics solutions.
In the role of an Azure data engineer, candidates facilitate stakeholders' understanding of the data through exploration, and design and maintain secure, compliant data processing pipelines using a variety of tools and methods. They use different Azure data services and frameworks to store and generate refined, augmented datasets for analysis.
As an Azure data engineer, they also contribute to ensuring that data pipelines and data stores are high-performing, efficient, well-organized, and dependable while adhering to specified business requirements and constraints. And, assist in identifying and resolving operational and data quality issues, and you design, implement, monitor, and optimize data platforms to align with the requirements of data pipelines.
Who should take the exam?
For Data Engineering on Microsoft Azure (DP-203) exam, candidates must have a solid knowledge of data processing languages, including:
- SQL
- Python
- Scala
They must have proficiency in parallel processing and data architecture patterns. And, knowledge of using the following for creating data processing solutions:
- Azure Data Factory
- Azure Synapse Analytics
- Azure Stream Analytics
- Azure Event Hubs
- Azure Data Lake Storage
- Azure Databricks
DP-203 Exam Course Outline
The Exam covers the given topics -
- Topic 1: Understand how to design and implement data storage (15–20%)
- Topic 2: Learn about developing data processing (40–45%)
- Topic 3: Understand about securing, monitoring, and optimizing data storage and data processing (30–35%)
Data Engineering on Microsoft Azure (DP-203) Exam FAQs
What is DP-203 exam?
The Data Engineering on Microsoft Azure (DP-203) is suitable for Data Engineers to integrate, transform, and consolidate data from various structured and unstructured data systems into structures that are suitable for building analytics solutions. 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.
How to prepare for Microsoft DP-203 exam?
The best way to prepare is to go through the exam study guide using the DP-203 exam tutorial and explore the various topics provided with documentation links for reference.
How many questions will there be on the exam?
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Who should I contact if I have questions concerning beta exam results?
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What are the skills required for DP-203 exam?
- Help stakeholders understand the data through exploration, and they build and maintain secure and compliant data processing pipelines by using different tools and techniques.
- Use various Azure data services and languages to store and produce cleansed and enhanced datasets for analysis.
- Also help ensure that data pipelines and data stores are high-performing, efficient, organized, and reliable, given a set of business requirements and constraints.
- Deal with unanticipated issues swiftly, and minimize data loss.
- Design, implement, monitor, and optimize data platforms to meet the data pipelines needs.
What is DP-203 exam format
- Exam Name: Data Engineering on Microsoft Azure
- Exam Code: DP-203
- Total Questions: 40-60 Questions
- Exam Duration: 120 mins
What is DP-203 Exam Outline?
The Data Engineering on Microsoft Azure (DP-203) Exam covers the following topics -
- Design and implement data storage (40-45%)
- Design and develop data processing (25-30%)
- Design and implement data security (10-15%)
- Monitor and optimize data storage and data processing (10-15%)
Who should take the DP-203 exam?
The Data Engineering on Microsoft Azure (DP-203) exam is suitable for candidates working as Data Engineering on Microsoft Azure with solid knowledge of data processing languages, such as SQL, Python, or Scala, and they need to understand parallel processing and data architecture patterns.