Potential Candidates and Requirements
According to the fact that this is an associate-level certification track, there are no strict requirements that you must fulfill except passing two exams. Thus, if you are planning to take Microsoft DP-200, you can be an Azure Data Engineer who wants to verify the skills to get certified or a specialist who is just starting an IT career. In both cases, you should have a solid knowledge of the content and be able to clear the tests with high results.
A potential candidate for the Microsoft DP-200 exam should know how to identify and meet the requirements by collaborating with the business stakeholders to implement data solutions that use Azure data services. Besides that, it is required that the individuals have the relevant skills in implementing various data solutions, such as Azure Blob storage, Azure Databricks, Azure Data Factory, Azure Stream Analytics, Azure Synapse Analytics, and Azure Cosmos DB. Also, they need to be able to ingest batch and streaming data, provision data storage services, identify performance bottlenecks, implement security requirements, access external data sources, transform data, and implement data retention policies. /
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Reference: https://www.microsoft.com/en-us/learning/exam-dp-200.aspx
Microsoft Implementing an Azure Data Solution Exam Certification Details:
| Duration | 120 mins |
| Exam Name | Microsoft Certified - Azure Data Engineer Associate |
| Books / Training | Course DP-200T01-A: Implementing an Azure Data Solution |
| Passing Score | 700 / 1000 |
| Number of Questions | 40-60 |
| Schedule Exam | Pearson VUE |
| Sample Questions | Microsoft Implementing an Azure Data Solution Sample Questions |
| Exam Code | DP-200 |
| Exam Price | $165 (USD) |
Microsoft DP-200 Exam Syllabus Topics:
| Topic | Details |
|---|---|
Implement Data Storage Solutions (40-45%) | |
| Implement non-relational data stores | - implement a solution that uses Cosmos DB, Data Lake Storage Gen2, or Blob storage -implement data distribution and partitions -implement a consistency model in Cosmos DB -provision a non-relational data store -provide access to data to meet security requirements -implement for high availability, disaster recovery, and global distribution |
| Implement relational data stores | -provide access to data to meet security requirements -implement for high availability and disaster recovery -implement data distribution and partitions for Azure Synapse Analytics -implement PolyBase |
| Manage data security | -implement data masking -encrypt data at rest and in motion |
Manage and Develop Data Processing (25-30%) | |
| Develop batch processing solutions | -develop batch processing solutions by using Data Factory and Azure Databricks -ingest data by using PolyBase -implement the integration runtime for Data Factory -create linked services and datasets -create pipelines and activities -create and schedule triggers -implement Azure Databricks clusters, notebooks, jobs, and autoscaling -ingest data into Azure Databricks |
| Develop streaming solutions | -configure input and output -select the appropriate built-in functions -implement event processing by using Stream Analytics |
Monitor and Optimize Data Solutions (30-35%) | |
| Monitor data storage | -monitor relational and non-relational data stores -implement Blob storage monitoring -implement Data Lake Storage Gen2 monitoring -implement Azure Synapse Analytics monitoring -implement Cosmos DB monitoring -configure Azure Monitor alerts -implement auditing by using Azure Log Analytics |
| Monitor data processing | -monitor Data Factory pipelines - monitor Azure Databricks -monitor Stream Analytics -configure Azure Monitor alerts -implement auditing by using Azure Log Analytics |
| Optimize of Azure data solutions | -troubleshoot data partitioning bottlenecks - optimize Data Lake Storage Gen2 -optimize Stream Analytics -optimize Azure Synapse Analytics -manage the data lifecycle |
Microsoft DP-200 Exam Syllabus Topics:
| Section | Objectives |
|---|---|
| Topic 1: Secure and optimize data solutions | - Monitor and optimize data systems
|
| Topic 2: Design and implement data storage | - Implement data storage solutions
|
| Topic 3: Develop data processing solutions | - Batch processing solutions
|
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