Microsoft DP-203 valid - in .pdf

DP-203 pdf
  • Exam Code: DP-203
  • Exam Name: Data Engineering on Microsoft Azure
  • Q & A: 365 Questions and Answers
  • PDF Price: $59.99
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  • Exam Code: DP-203
  • Exam Name: Data Engineering on Microsoft Azure
  • Q & A: 365 Questions and Answers
  • PDF Version + PC Test Engine + Online Test Engine
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Microsoft DP-203 valid - Testing Engine

DP-203 Testing Engine
  • Exam Code: DP-203
  • Exam Name: Data Engineering on Microsoft Azure
  • Q & A: 365 Questions and Answers
  • Software Price: $59.99
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About Microsoft Data Engineering on Microsoft Azure - DP-203 exam braindumps

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Microsoft DP-203 (Designing Data Platform Solutions on Microsoft Azure) certification is a great way to validate your hands-on experience with cloud platforms, data architecture, and database technologies. This exam is designed to determine the competency of candidates to design distributed data solutions for enterprise applications based on Azure technologies. Before you begin studying for this exam, there are a few terms that you should familiarize yourself with. First and foremost, you should understand the difference between Microsoft SQL Server, Microsoft SQL Database, and Microsoft Azure SQL Database. Exam objectives are associated with these products; therefore, it is important to know when you will be tested on each one. Microsoft DP-203 Dumps tests a candidate's knowledge of Microsoft technologies used for building a data platform and cloud environment. Microsoft DP-203 is a certification of data engineers. The certification exam is designed to test the skills of designing, deploying, and managing Microsoft HDInsight and Microsoft Azure SQL Data Warehouse clusters for business intelligence and analytics solutions.

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

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Microsoft DP-203 Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Design and Implement Data Storage40-45%- Implement data storage solutions
  • 1. Implement Azure Data Lake Storage
  • 2. Implement Azure Synapse Analytics storage
  • 3. Implement Delta Lake
- Design a data storage structure
  • 1. Design a data warehouse schema
  • 2. Design a lakehouse architecture
  • 3. Design partitioning strategies
Topic 2: Design and Implement Data Security10-15%- Implement data security
  • 1. Manage sensitive data
  • 2. Configure authentication and authorization
  • 3. Implement data encryption
Topic 3: Design and Develop Data Processing25-30%- Develop batch processing solutions
  • 1. Develop Azure Data Factory pipelines
  • 2. Transform data using Apache Spark
  • 3. Implement Azure Databricks notebooks
- Develop stream processing solutions
  • 1. Implement Azure Stream Analytics
  • 2. Process streaming data with Event Hubs
Topic 4: Monitor and Optimize Data Storage and Processing10-15%- Monitor data solutions
  • 1. Monitor Spark workloads
  • 2. Monitor pipeline execution
- Optimize performance
  • 1. Optimize data partitioning and indexing
  • 2. Optimize query performance

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