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Completing the Microsoft DP-201 exam and obtaining the Microsoft Certified: Azure Data Engineer Associate certification provides the individuals with numerous prospects. First of all, this certificate prepares the professionals for a career of an Azure Data Engineer. However, the holders of this associate-level option can also explore other positions. The job titles that will also become available to you after getting certified include an Azure Cloud Engineer, a Cloud Solutions Systems Engineer, a Software Engineer, a DevOps Engineer, a Senior DevOps Engineer, among others. According to PayScale, an Azure Data Engineer with 1-4 years of experience earns an average of $90,496 per year, but the same professional with 5-9 years of experience can get about $98,686 per annum.
Reference: https://www.microsoft.com/en-us/learning/exam-dp-201.aspx
Skills measured
- Design for data security and compliance (25-30%)
- Design data processing solutions (25-30%)
- Design Azure data storage solutions (40-45%)
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The content of the Microsoft DP-201 exam revolves around three main topics that comprise of specific knowledge and skills that the candidates should develop before sitting for the test. The details of these areas are outlined below:
Topic 1. Designing Azure Data Storage Solutions (40-45%)
- Designing relational Cloud data stores: this subtopic focuses on such abilities as designing data partitions and distribution; designing for scale (comprising latency & throughput); designing a solution that utilizes Azure Synapse Analytics; designing a strategy for disaster recovery; designing for high availability.
- Designing non-relational Cloud data stores: here the applicants should demonstrate that they are capable of designing data partitions and distribution; designing for scale (latency, throughput, and multi-region); designing a solution that utilizes Data Lake Storage Gen2, Blob storage, or Cosmos DB; selecting the proper Cosmos DB API; designing a strategy for disaster recovery; designing for high availability.
- Suggesting an Azure data storage solution in compliance with given requirements: this requires one’s skills in choosing the proper data storage solution in order to satisfy the technical & business requirements; choosing the type of partition distribution.
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Microsoft DP-201 Exam Syllabus Topics:
| Topic | Details |
|---|---|
Design Azure Data Storage Solutions (40-45%) | |
| Recommend an Azure data storage solution based on requirements | - choose the correct data storage solution to meet the technical and business requirements - choose the partition distribution type |
| Design non-relational cloud data stores | - design data distribution and partitions - design for scale (including multi-region, latency, and throughput) - design a solution that uses Cosmos DB, Data Lake Storage Gen2, or Blob storage - select the appropriate Cosmos DB API - design a disaster recovery strategy - design for high availability |
| Design relational cloud data stores | - design data distribution and partitions - design for scale (including latency and throughput) - design a solution that uses Azure Synapse Analytics - design a disaster recovery strategy - design for high availability |
Design Data Processing Solutions (25-30%) | |
| Design batch processing solutions | - design batch processing solutions that use Data Factory and Azure Databricks - identify the optimal data ingestion method for a batch processing solution - identify where processing should take place, such as at the source, at the destination, or in transit |
| Design real-time processing solutions | - design for real-time processing by using Stream Analytics and Azure Databricks - design and provision compute resources |
Design for Data Security and Compliance (25-30%) | |
| Design security for source data access | - plan for secure endpoints (private/public) - choose the appropriate authentication mechanism, such as access keys, shared access signatures (SAS), and Azure Active Directory (Azure AD) |
| Design security for data policies and standards | - design data encryption for data at rest and in transit - design for data auditing and data masking - design for data privacy and data classification - design a data retention policy - plan an archiving strategy - plan to purge data based on business requirements |
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