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DP-800 In preparation

SQL AI Developer

DP-800 covers database development on SQL Server, Azure SQL and SQL database in Fabric: advanced T-SQL, security and CI/CD, and AI capabilities such as embeddings, vector search and retrieval-augmented generation.

Microsoft Certified: SQL AI Developer Associate · for database developers who add AI features to SQL solutions

Level
Associate
Exam time
100 minutes
Passing score
700 of 1000
Microsoft Learn during the exam
Allowed
Skills outline
October 19, 2026

Study cards and questions for DP-800 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-800 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 19, 2026. The percentage is the share of the exam.

1 Design and develop database solutions

35-40%

Design and implement database objects

  • Design and implement tables, including data types, size, columns, indexes, and columnstore indexes
  • Design and implement specialized tables, including in-memory, temporal, external, ledger, and graph
  • Design and implement JSON columns and indexes
  • Design and implement database constraints, including PRIMARY KEY, FOREIGN KEY, UNIQUE, CHECK, and DEFAULT
  • Design and implement SEQUENCES
  • Design and implement partitioning for tables and indexes

Implement programmability objects

  • Create views
  • Create scalar functions
  • Create table-valued functions
  • Create stored procedures
  • Create triggers

Write advanced T-SQL code

  • Write common table expressions (CTEs)
  • Write queries that include window functions
  • Write queries that include JSON functions, such as JSON_OBJECT, JSON_ARRAY, JSON_ARRAYAGG, JSON_CONTAINS, OPENJSON, and JSON_VALUE
  • Write queries that include regular expressions, such as REGEXP_LIKE, REGEXP_REPLACE, REGEXP_SUBSTR, REGEXP_INSTR, REGEXP_COUNT, REGEXP_MATCHES, and REGEXP_SPLIT_TO_TABLE
  • Write queries that include fuzzy string matching functions, such as EDIT_DISTANCE, EDIT_DISTANCE_SIMILARITY, and JARO_WINKLER_DISTANCE
  • Write graph queries that use the MATCH operator
  • Write correlated queries
  • Implement error handling

Design and implement SQL solutions by using AI-assisted tools

  • Interpret security impact of using AI-assisted tools
  • Enable GitHub Copilot and Copilot in Fabric
  • Configure model and Model Context Protocol (MCP) tool options in a GitHub Copilot or Copilot in Fabric chat session
  • Create and configure GitHub Copilot instruction files
  • Connect to MCP server endpoints, including Microsoft SQL Server and Fabric lakehouse

2 Secure, optimize, and deploy database solutions

35-40%

Implement data security and compliance

  • Design and implement data encryption, including Always Encrypted and column-level encryption
  • Design and implement Dynamic Data Masking
  • Design and implement Row-Level Security (RLS)
  • Design and implement object-level permissions
  • Implement secure database access, including passwordless
  • Implement auditing
  • Secure model endpoints, including Managed Identity
  • Secure GraphQL, REST, and MCP endpoints

Optimize database performance

  • Recommend database configurations
  • Preserve data integrity and consistency by using transaction isolation levels and concurrency controls
  • Evaluate query performance by using query execution plans, dynamic management views (DMVs), Query Store, and Query Performance Insight
  • Identify and resolve query performance issues, including blocking and deadlocks

Implement CI/CD by using SQL Database Projects

  • Design and implement a testing strategy, including unit tests and integration tests
  • Create and manage reference/static data in source control
  • Create, build, and validate database models by using SQL Database Projects, including SDK-style models
  • Configure source control for SQL Database Projects
  • Manage branching, pull requests, and conflict resolution
  • Implement secrets management
  • Detect schema drift by using SQL Database Projects
  • Update an SQL database project and deploy changes
  • Design and implement controls for deployment pipelines, including branching policies, triggers in approvals, authentication tables, and code owners

Integrate SQL solutions with Azure services

  • Create configuration files for Data API builder (DAB)
  • Configure entities for REST and GraphQL, including data caching, pagination, searching, and filtering
  • Configure REST or GraphQL endpoints
  • Expose database objects, stored procedures, and views, including GraphQL relationships
  • Configure and implement DAB deployment
  • Recommend Azure Monitor configurations, including Application Insights and Log Analytics
  • Handle changes by using change event streaming (CES), change data capture (CDC), Change Tracking, Azure Functions with SQL trigger binding, or Azure Logic Apps

3 Implement AI capabilities in database solutions

25-30%

Design and implement models and embeddings

  • Evaluate external models, including multimodal, multilanguage, sizes, and structured output
  • Create and manage external models
  • Choose an embedding maintenance method, including table triggers, Change Tracking, Azure Functions with SQL trigger binding, Azure Logic Apps, CDC, and Microsoft Foundry
  • Identify which columns to include in embeddings
  • Design and implement chunks for embeddings
  • Generate embeddings

Design and implement intelligent search

  • Choose from full-text, semantic vector, and hybrid search
  • Implement full-text search
  • Design for vector data, including vector data type, vector indexes, and size
  • Identify when to use vector-related types and functions for semantic searching, including VECTOR_NORMALIZE, VECTOR_DISTANCE, VECTORPROPERTY, and VECTOR_SEARCH
  • Choose between using ANN and KNN for vector search
  • Evaluate vector index types and metrics
  • Implement vector search
  • Implement hybrid search
  • Implement reciprocal rank fusion (RRF)
  • Evaluate performance of vector and hybrid search

Design and implement retrieval-augmented generation (RAG)

  • Identify use cases for RAG
  • Create a prompt by using the sp_invoke_external_rest_endpoint stored procedure
  • Convert structured data to JSON for language model processing
  • Send results to language model
  • Extract language model responses