Microsoft Designing and Implementing Cloud-Native Applications Using Microsoft Azure Cosmos DB (DP-420日本語版) : DP-420日本語

DP-420日本語 real exams

Exam Code: DP-420J

Exam Name: Designing and Implementing Cloud-Native Applications Using Microsoft Azure Cosmos DB (DP-420日本語版)

Updated: Aug 19, 2026

Q & A: 281 Questions and Answers

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A section outlining the topics of a Microsoft DP-420 Certification Exam

DP-420 Dumps cover the following topics of the Microsoft DP-420 Certification Exam:

  • Design and execute information models: 40%
  • Optimize an Azure Cosmos DB solution: 20%
  • Maintain an Azure Cosmos DB solution: 20%
  • Design and implement data distribution: 10%
  • Integrate an Azure Cosmos DB solution: 10%

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

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Skills measured

  • Design and implement data distribution (5–10%)
  • Design and implement data models (35–40%)
  • Maintain an Azure Cosmos DB solution (25–30%)
  • The English language version of this exam will be updated on August 4, 2022. Please download the exam skills outline below to see what’s changing.
  • Optimize an Azure Cosmos DB solution (15–20%)
  • Integrate an Azure Cosmos DB solution (5–10%)

Who should take the Microsoft DP-420 Certification Exam?

The targeted audience of the Microsoft DP-420 Certification Exam is IT professionals, who are interested in designing and implementing cloud-native applications using Microsoft Azure Cosmos DB database technologies. The Microsoft DP-420 Certification Exam is an important certification exam for every IT professional, who wants to get a job in a reputable company. DP-420 Dumps suggest that those individuals who have job titles like Azure SQL Database Administrator, Azure SQL Database Developer, Azure Cosmos DB Administrator, and Azure Cosmos DB Developer can also take this certification exam.

Microsoft DP-420日本語 Exam Syllabus Topics:

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

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