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DP-420 · Official In preparation

Building AI Applications with Azure Cosmos DB

DP-420 covers designing and developing Azure Cosmos DB solutions, securing and optimizing production workloads, and implementing AI retrieval, RAG, agent memory, and Microsoft Fabric analytics integrations.

Microsoft Certified: Azure Cosmos DB AI Developer Associate · for developers who build secure, scalable Azure Cosmos DB applications and AI retrieval, RAG, and agent-memory solutions

Level
Associate
Exam time
100 minutes
Passing score
700 of 1000
Microsoft Learn during the exam
Allowed
Skills outline
October 6, 2026

Study cards and questions for DP-420 are being written.

The official skills outline is below, so you can already see what the exam covers. Until this exam is ready, the best free preparation is the learning path on Microsoft Learn.

DP-420 on Microsoft Learn ↗

Skills measured

What the exam covers

The domains and objectives as Microsoft lists them in the official study guide, version of October 6, 2026. The percentage is the share of the exam.

1 Design and develop database solutions

40-45%

Evaluate resource models and SDK solutions

  • Evaluate consistency levels
  • Evaluate Request Unit (RU) consumption requirements
  • Evaluate and configure appropriate throughput
  • Configure account-level and container-level settings
  • Create and manage databases and containers
  • Initialize SDK clients and connections

Implement database operations

  • Implement queries, parameterized queries, paging, and continuation tokens
  • Implement point read operations
  • Implement create, replace, update, patch, and delete operations
  • Implement bulk, batch, and transactional operations
  • Configure Time to Live (TTL)
  • Implement concurrency control mechanisms

Design and implement data models and partitioning strategies

  • Design data models for access patterns and operational requirements
  • Design and select partition keys based on cardinality, access patterns, and write distribution requirements
  • Determine when to embed or reference related data
  • Evaluate the impact of partition key selection on scalability and performance
  • Implement hierarchical partition keys (HPKs) and multi-tenant partitioning strategies
  • Implement synthetic partition keys
  • Identify partitioning anti-patterns and partitioning issues during solution reviews
  • Estimate workload cost requirements

Implement change feed

  • Implement change feed operations
  • Differentiate between change feed processor and pull mode models
  • Configure lease containers
  • Implement change feed processing using latest mode or all versions and delete
  • Implement copy container jobs

Design and implement Azure Cosmos DB solutions by using AI-assisted tools

  • Implement Agent Kit to follow Azure Cosmos DB development recommended solutions
  • Implement Agent Kit to accelerate AI application development

2 Secure, optimize, and deploy database solutions

30-35%

Implement database security controls

  • Configure authentication mechanisms
  • Configure authorization controls
  • Configure network security
  • Configure dynamic data masking

Implement availability and recovery solutions

  • Configure backup and restore solutions
  • Implement multi-region read and write configurations
  • Configure partition failover solutions

Optimize and remediate database performance

  • Optimize query performance
  • Analyze query performance metrics
  • Analyze index performance metrics
  • Monitor end-to-end latency against service objectives
  • Configure Azure Monitor alerts
  • Analyze diagnostic logs in Log Analytics

Implement Fleet management and monitoring across multiple accounts and subscriptions

  • Configure Fleet level throughput management
  • Configure Fleet monitoring with fleet analytics

3 Implement AI and analytics capabilities in database solutions

20-25%

Design and implement AI retrieval

  • Design and implement full-text search
  • Design for vector data, including vector data type, vector indexes, and size
  • Design and implement vector partitioning and sharded DiskANN implementations
  • Implement vector search
  • Design and implement hybrid search
  • Optimize full-text and vector performance
  • Design and implement Retrieval-Augmented Generation (RAG)

Design and implement agent memory stores

  • Design durable memory stores
  • Implement conversation state
  • Implement semantic memory

Design and implement operational analytics on Azure Cosmos DB

  • Choose between Azure Cosmos DB and Cosmos DB in Microsoft Fabric
  • Configure and monitor data mirroring into Microsoft Fabric
  • Query Azure Cosmos DB mirrored JSON data in Microsoft Fabric with T-SQL and Spark
  • Read and write data into Azure Cosmos DB from Microsoft Fabric with Cosmos DB Spark connector