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Reference: https://docs.microsoft.com/en-us/learn/certifications/exams/dp-420?WT.mc_id=Azure_blog-wwl

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Microsoft DP-420 Korean Exam Syllabus Topics:

SectionWeightObjectives
Maintain an Azure Cosmos DB solution25-30%- Monitor and troubleshoot Azure Cosmos DB solutions
  • 1. Troubleshoot throughput issues
  • 2. Implement backup and recovery strategies
  • 3. Monitor metrics and logs
  • 4. Monitor performance and availability
Design and implement data distribution5-10%- Design and implement multi-region writes
  • 1. Implement multi-region writes
  • 2. Choose when to use multi-region writes
  • 3. Implement conflict resolution policies
- Design and implement replication strategy
  • 1. Choose when to distribute data
  • 2. Define automatic failover policies
  • 3. Perform manual failovers
  • 4. Choose a consistency model
  • 5. Evaluate consistency model impacts
Design and implement data models35-40%- Implement client connectivity options
  • 1. Use Azure Cosmos DB emulator
  • 2. Create database connections
  • 3. Implement singleton client
  • 4. Configure client-side threading and logging
  • 5. Choose gateway or direct connectivity mode
  • 6. Handle connection errors
- Implement server-side programming
  • 1. Implement user-defined functions
  • 2. Write and deploy stored procedures
  • 3. Implement triggers
- Implement data access
  • 1. Implement SQL queries with arrays and aggregation
  • 2. Manage transactional batch operations
  • 3. Implement optimistic concurrency control
  • 4. Use SDKs for CRUD operations
  • 5. Implement patch operations
  • 6. Use consistency and query request options
- Design a data partitioning strategy
  • 1. Plan for transactions with partition keys
  • 2. Choose a partition key
  • 3. Evaluate throughput and data distribution
  • 4. Evaluate cross-partition query cost
  • 5. Choose a partitioning strategy
  • 6. Implement synthetic partition keys
- Design and implement a non-relational data model for Azure Cosmos DB for NoSQL
  • 1. Store multiple related entities in the same document
  • 2. Store multiple entity types in the same container
  • 3. Identify partition key, id, and unique keys
  • 4. Reference between documents
  • 5. Denormalize data across documents
  • 6. Identify data and associated access patterns
- Plan and implement sizing and scaling
  • 1. Choose between serverless, provisioned, and free tier
  • 2. Configure throughput
  • 3. Evaluate throughput and storage requirements
  • 4. Design resource governance and scaling
  • 5. Evaluate global distribution costs
Optimize an Azure Cosmos DB solution15-20%- Design and implement change feeds
  • 1. Consume change feeds using SDK
  • 2. Manage change feed instances
  • 3. Develop Azure Functions triggers
  • 4. Implement aggregation persistence
- Optimize query performance
  • 1. Implement integrated cache
  • 2. Retrieve request unit cost
  • 3. Calculate query cost
  • 4. Adjust indexing policies
- Define and implement indexing strategy
  • 1. Choose read-heavy or write-heavy indexing
  • 2. Choose appropriate index types
  • 3. Configure custom indexing policies
  • 4. Implement composite indexes
  • 5. Optimize index performance
Integrate an Azure Cosmos DB solution5-10%- Enable analytical workloads
  • 1. Query analytical store from Synapse
  • 2. Write data back from Spark
  • 3. Configure Azure Cosmos DB Mirroring
  • 4. Enable analytical store
- Implement solutions across services
  • 1. Enforce referential integrity
  • 2. Aggregate and archive data
  • 3. Implement Azure AI Search integration
  • 4. Integrate Azure Functions and Event Hubs
  • 5. Use Change Feed for denormalization

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