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Preparation Options
Microsoft offers useful preparation options for the individuals who are planning to start their journey with this provider. Thus, for the Microsoft DP-200 exam, you can go for the following resources:
- Paid instructor-led courses
As for the courses with an instructor, you can go for the Implementing an Azure Data Solution training. There are the options that include the classroom training and virtual courses. The time for each session is based on the location of the course.
- Study guides
Using the study guides is what you need to do by all means. They help you cover the details of the exam content, its topics, and the information related to the format of the test. You can search for the options offered by Microsoft or look for the ones that are available on Amazon. For example, you can buy Azure Storage, Streaming, and Batch Analytics: A guide for data engineers by Richard L. Nuckolls or Hands-On Data Warehousing with Azure Data Factory by Christian Coté.
- Free online learning paths
There are 8 learning paths in total that you can follow to learn the details of the exam content. Each path has several modules to cover. Thus, you can go for Store Data in Azure, Azure for the Data Engineer, Work with NoSQL Data in Azure Cosmos DB, Work with Relational Data in Azure, Implement a Data Streaming Solution with Azure Streaming Analytics, Large-Scale Data Processing, Implement a Data Warehouse with Azure Synapse Analytics, and Data Engineering with Azure Databricks.
Reference: https://www.microsoft.com/en-us/learning/exam-dp-200.aspx
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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 |
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