Implementing an Azure Data Solution (DP-200) 

About Implementing an Azure Data Solution (DP-200) Exam

DP-200 exam measures your ability to accomplish the following technical tasks: implement data storage solutions; manage and develop data processing; and monitor and optimize data solutions. Candidates planning to take DP-200 exam are Azure data engineers responsible for data-related implementation tasks that include -

• Provisioning data storage services
• Ingesting streaming and batch data
• Transforming data
• Implementing security requirements
• Implementing data retention policies
• Identifying performance bottlenecks
• Accessing external data sources.

Who should take this exam?

DP-200 exam is suitable for Microsoft Azure data engineers who are responsible to collaborate with business stakeholders to identify and meet the data requirements to implement data solutions that use Azure data services. The candidates for this exam must be able to implement data solutions that use the following Azure services: Azure Cosmos DB, Azure Synapse Analytics (formerly Azure SQL DW),  Azure Data Factory, Azure Stream Analytics, Azure Databricks, and Azure Blob storage.

Exam Format

• Exam Name: Implementing an Azure Data Solution 
• Exam Code: DP-200
• Exam: 180 minutes
• Exam Format: Multiple Choice and Multi-Response Questions
• Exam Type: Azure
• Number of Questions: 40-60 Questions
• Eligibility/Pre-requisite: NIL
• Exam Fee: $165 USD*
• Exam Language: English, Japanese, Chinese (Simplified), Korean
• Retirement Date: None
  

Course Outline Exam DP-200: Implementing an Azure Data Solution

The Implementing an Azure Data Solution (DP-200) exam covers latest exam updates and topics - 

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

• Configure elastic pools
• Configure geo-replication
• Provide access to data to meet security requirements
• Implement for high availability, disaster recovery, and global distribution
• 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 windowing functions
• Implement event processing by using Stream Analytics
• Ingest and query streaming data with Azure Data Explorer
  

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 SQL Database monitoring
• Implement Azure Synapse Analytics monitoring
• Implement Cosmos DB monitoring
• Implement Azure Data Explorer 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 Azure data solutions

• Troubleshoot data partitioning bottlenecks
• Optimize Data Lake Storage Gen2 
• Optimize Stream Analytics
• Optimize Azure Synapse Analytics
• Optimize SQL Database
• Manage the data lifecycle

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