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Microsoft Designing and Implementing Cloud-Native Applications Using Microsoft Azure Cosmos DB (DP-420 Korean Version) : DP-420 Korean Exam

DP-420 Korean Exam Questions
  • Exam Code: DP-420-KR
  • Exam Name: Designing and Implementing Cloud-Native Applications Using Microsoft Azure Cosmos DB (DP-420 Korean Version)
  • Updated: Aug 14, 2026
  • Q & A: 281 Questions and Answers
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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 certification exam is a valuable credential for professionals who want to demonstrate their expertise in designing and implementing cloud-native applications using Azure Cosmos DB. To prepare for the exam, candidates should have a solid understanding of cloud computing, distributed systems, and NoSQL databases, as well as experience working with Azure Cosmos DB. Earning the DP-420 certification can help professionals advance their careers in cloud computing and stay up-to-date with the latest trends and best practices in cloud-native application development.

Microsoft DP-420 Korean Exam Syllabus Topics:

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

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