Microsoft DP-203 Q&A - in .pdf

  • DP-203 pdf
  • Exam Code: DP-203
  • Exam Name: Data Engineering on Microsoft Azure
  • Updated: Aug 20, 2026
  • Q & A: 365 Questions and Answers
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  • Exam Code: DP-203
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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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Data Engineering on Microsoft Azure Exam Certification Details:

Duration150 mins
Exam CodeDP-203
Number of Questions40-60
Exam Price$165 (USD)
Passing Score700 / 1000
Books / TrainingDP-203T00: Data Engineering on Microsoft Azure
Sample QuestionsData Engineering on Microsoft Azure Sample Questions
Exam NameMicrosoft Certified - Azure Data Engineer Associate
Schedule ExamPearson VUE

Microsoft DP-203 Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Design and Develop Data Processing25-30%- 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
Topic 2: Design and Implement Data Storage40-45%- Implement data storage solutions
  • 1. Implement Azure Data Lake Storage
  • 2. Implement Delta Lake
  • 3. Implement Azure Synapse Analytics storage
- Design a data storage structure
  • 1. Design a data warehouse schema
  • 2. Design partitioning strategies
  • 3. Design a lakehouse architecture
Topic 3: Design and Implement Data Security10-15%- Implement data security
  • 1. Configure authentication and authorization
  • 2. Implement data encryption
  • 3. Manage sensitive data
Topic 4: Monitor and Optimize Data Storage and Processing10-15%- Monitor data solutions
  • 1. Monitor pipeline execution
  • 2. Monitor Spark workloads
- Optimize performance
  • 1. Optimize query performance
  • 2. Optimize data partitioning and indexing
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