DP-200: Implementing an Azure Data Solution

You will learn how to implement data processing technologies, big data, data security, monitoring and processing. You will also learn how to troubleshoot these technologies and plan for disaster recovery.

Course cost: $2,245

Course length:

Full time – 3 days: Monday-Thursday 9:00AM-5:00PM

Part time – 5 days – Tuesday and Thursday 6:00PM-9:30PM and Saturday 9:00AM-5:00PM

This course covers the following areas:

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
The exam guide below shows the changes that were implemented on November 24, 2020.
Audience Profile
Candidates for this exam are Microsoft Azure data engineers who collaborate with business
stakeholders to identify and meet the data requirements to implement data solutions that use
Azure data services.
Azure data engineers are 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, and accessing external data sources.
Candidates for this exam must be able to implement data solutions that use the following Azure
services: Azure Cosmos DB, Azure SQL Database, Azure Synapse Analytics (formerly Azure SQL
DW), Azure Data Lake Storage, Azure Data Factory, Azure Stream Analytics, Azure Databricks,
and Azure Blob storage.

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 storessources
– 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