Welcome to the DP-800: Developing AI-Enabled Database Solutions Exam Prep Hub!

Welcome to the one-stop hub with information for preparing for the DP-800: Developing AI-Enabled Database Solutions certification exam. The content for this exam helps prepare you to have “subject matter expertise in designing and developing AI-enabled database solutions across Microsoft SQL platforms, including Microsoft SQL Server, Azure SQL, and SQL databases in Microsoft Fabric”.
Upon successful completion of the exam, you earn the Microsoft Certified: SQL AI Developer Associate certification.

This hub provides information directly here (topic-by-topic as outlined in the official study guide), links to a number of external resources, tips for preparing for the exam, practice tests, and section questions to help you prepare. Bookmark this page and use it as a guide to ensure that you are fully covering all relevant topics for the DP-800 exam and making use of as many of the resources available as possible.

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Audience profile (from Microsoft’s site)

As a candidate for this Microsoft Certification, you should have subject matter expertise in designing and developing AI-enabled database solutions across Microsoft SQL platforms, including Microsoft SQL Server, Azure SQL, and SQL databases in Microsoft Fabric.

You should also have experience writing T-SQL code and developing databases in Microsoft SQL platforms. Plus, you need to be familiar with continuous integration and continuous deployment (CI/CD) practices in GitHub, AI-assisted development tools, and AI concepts, such as embeddings, vectors, and models.

Your responsibilities include:
- Designing and developing database solutions that include both structured and semi-structured data.
- Integrating AI features into modern and highly scalable enterprise applications.
- Securing, optimizing, and deploying database solutions.
- Implementing AI capabilities in database solutions.

You work closely with application developers; database administrators (DBAs); architects; AI engineers; development, security, operations (DevSecOps) engineers; security and compliance administrators; and other stakeholders to deliver robust, high-performance database solutions that power modern applications and AI-driven experiences.

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Skills at a glance (as specified in the official study guide)

• Design and develop database solutions (35–40%)
• Secure, optimize, and deploy database solutions (35–40%)
• Implement AI capabilities in database solutions (25–30%)

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Topic-by-Topic Exam Content

[click a topic link to access the content and practice questions for that topic]

Design and develop database solutions (35–40%)

Design and implement database objects

• Design and implement tables, including data types, size, columns, indexes, and column store 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 Microsoft Copilot in Fabric
• Configure model and Model Context Protocol (MCP) tool options in a GitHub Copilot or Copilot in Fabric chat session
  • Configure model and MCP tool options in a GitHub Copilot or Copilot in Fabric chat session – Part 1
  • Configure model and MCP tool options in a GitHub Copilot or Copilot in Fabric chat session – Part 2
  • Configure model and MCP tool options in a GitHub Copilot or Copilot in Fabric chat session – Part 3
• Create and configure GitHub Copilot instruction files
• Connect to MCP server endpoints, including Microsoft SQL Server and Fabric lakehouse

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
  • Implement auditing – Part 1
  • Implement auditing – Part 2
  • Implement auditing – Part 3
• 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
  • Identify and resolve query performance issues, including blocking and deadlocks – Part 1
  • Identify and resolve query performance issues, including blocking and deadlocks – Part 2
  • Identify and resolve query performance issues, including blocking and deadlocks – Part 3

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
  • Create, build, and validate database models by using SQL Database Projects, including SDK-style models – Part 1
  • Create, build, and validate database models by using SQL Database Projects, including SDK-style models – Part 2
• 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 a SQL database project and deploy changes
• Design and implement controls for deployment pipelines, including branching policies, triggers in approvals, authentication tables, and code owners
  • Design and implement controls for deployment pipelines, including branching policies, triggers in approvals, authentication tables, and code owners – Part 1
  • Design and implement controls for deployment pipelines, including branching policies, triggers in approvals, authentication tables, and code owners – Part 2
  • Design and implement controls for deployment pipelines, including branching policies, triggers in approvals, authentication tables, and code owners – Part 3

Integrate SQL solutions with Azure services

• Create configuration files for Data API builder (DAB)
  • Create configuration files for Data API builder (DAB) – Part 1
  • Create configuration files for Data API builder (DAB) – Part 2
• 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

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, CES, and Microsoft Foundry
  • Choose an embedding maintenance method, including table triggers, Change Tracking, Azure Functions with SQL trigger binding, Azure Logic Apps, CDC, CES, and Microsoft Foundry – Part 1
  • Choose an embedding maintenance method, including table triggers, Change Tracking, Azure Functions with SQL trigger binding, Azure Logic Apps, CDC, CES, and Microsoft Foundry – Part 2
• 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 ENN 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

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DP-800 Practice Exams

• DP-800 Practice Exam #1 (30 questions)
• DP-800 Practice Exam #2 (30 questions)
• DP-800 Practice Exam #3 (30 questions)
• DP-800 Practice Exam #4 (30 questions)

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Important DP-800 Resources

Link to the free, comprehensive, self-paced course on Microsoft Learn:
Course: Develop AI-enabled database solutions

Course DP-800T00-A: Develop AI-enabled database solutions – Training | Microsoft Learn

This course has 3 learning paths. The 3 learning paths and their modules are listed with links below:

(1) Design and develop database solutions

This learning path has 4 modules:
(i) Design and implement database objects with SQL
(ii) Implement programmability objects with SQL
(iii) Write advanced T-SQL code
(iv) Implement SQL solutions by using AI-assisted tools

(2) Secure, optimize, and deploy database solutions

This learning path has 4 modules:
(i) Implement data security and compliance with SQL
(ii) Optimize database performance
(iii) Implement CI/CD by using SQL Database Projects
(iv) Integrate SQL solutions with Azure services

(3) Implement AI capabilities in database solutions

This learning path has 3 modules:
(i) Design and implement models and embeddings with SQL
(ii) Design and implement intelligent search with SQL
(iii) Design and implement RAG with SQL

Link to the certification page:

Link to the “Microsoft Certified: SQL AI Developer Associate” certification page:
https://learn.microsoft.com/en-us/credentials/certifications/developing-ai-enabled-database-solutions/?practice-assessment-type=certification

Link to the study guide:

Link to the Study Guide for DP-800: Developing AI-Enabled Database Solutions:
https://learn.microsoft.com/en-us/credentials/certifications/resources/study-guides/dp-800

YouTube resources:

Get Certified: SQL AI Developer (DP-800) series by Microsoft Reactor

Courses:

These are two highly rated courses for DP-800 on Udemy:

• DP-800 exam prep: Microsoft SQL Server AI Developer
• DP-800: Microsoft SQL AI Developer Associate Exam Prep

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Good luck to you passing the DP-800 Exam!
However, the more preparation you have, the less luck you will need. 🙂

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