DP-800T00: Develop AI-enabled database solutions

Course Description

This course teaches developers and database professionals how to design and build AI-enabled database solutions using Microsoft SQL platforms, including SQL Server, Azure SQL, and SQL databases in Microsoft Fabric. Students learn advanced T-SQL development, database security, performance optimization, CI/CD for SQL Database Projects, Azure service integration, and AI patterns such as embeddings, vector search, hybrid search, and retrieval-augmented generation, or RAG

Why choose Dynamics Edge for DP-800T00 training?

Dynamics Edge turns Microsoft course topics into practical instructor-led training for learners who need job skills, certification preparation, course review, and project-ready capability. The class can be delivered as a public course, private team course, government training, or customized workshop.

• Learn from a Microsoft-focused training provider with practical business applications, cloud, security, data, AI, and Power Platform delivery experience.
• Use structured course review, hands-on discussion, and implementation examples instead of only reading a catalog outline.
• Request private team delivery for role-based learning, project onboarding, government teams, migration work, or enterprise adoption.

What will you learn in DP-800T00 training?

This course helps learners understand the official Microsoft topics and apply them through clear outcomes, instructor discussion, practice activities, and review questions.

• Review product SQL Server and apply it to practical course scenarios.
• Review role Data Analyst Data Engineer Data Scientist Database Administrator and apply it to practical course scenarios.
• Review subject Artificial intelligence Databases and apply it to practical course scenarios.
• Review languages English Chinese (Simplified) French German Japanese Korean Portuguese (Brazil) Spanish and apply it to practical course scenarios.
• Review course Duration 3 days and apply it to practical course scenarios.
• Review related certifications Microsoft Certified: SQL AI Developer Associate and apply it to practical course scenarios.
• Review overview and apply it to practical course scenarios.
• Review audience Profile and apply it to practical course scenarios.
• Review skills earned upon completion and apply it to practical course scenarios.
• Review course Syllabus and apply it to practical course scenarios.

DP-800T00 Course Outline

Learning Path 1: Design and develop database solutions

Module 1: Design and implement database objects with SQL

Students learn how to design and create core SQL database objects for modern applications. This includes choosing the right SQL platform, designing tables, selecting data types, creating indexes, enforcing integrity, using JSON columns, and implementing partitioning strategies.

Topics include:

• SQL Server, Azure SQL Database, Azure SQL Managed Instance, and SQL database in Microsoft Fabric platform choices.
• Table design for structured and semi-structured data.
• Numeric, string, date/time, and JSON data types.
• Primary keys, foreign keys, constraints, and sequence objects.
• Rowstore and columnstore indexes.
• Specialized table types, including temporal and memory-optimized tables.
• JSON columns for flexible application data.
• Table partitioning for large-scale data management.

Lab: Design and implement database objects with SQL
Students build an e-commerce database schema using SQL Server 2025. They create constrained tables, implement a temporal table for price history, use JSON-typed columns for metadata, configure date-based partitioning, and generate identifiers with sequences.

Module 2: Implement programmability objects with SQL

Students learn how to create reusable database logic with views, stored procedures, user-defined functions, and triggers. They compare use cases for each programmability object and learn how to choose the right object for maintainability, security, and performance.

Topics include:

• Creating views to simplify complex queries.
• Creating stored procedures with parameters and transaction logic.
• Creating scalar and table-valued functions.
• Creating triggers for automated database actions.
• Choosing between views, stored procedures, functions, and triggers.
• Avoiding performance problems with nested views and scalar functions.
• Designing programmable SQL objects for application access patterns.

Lab: Implement programmability objects with SQL
Students create views, stored procedures, functions, and triggers to support application logic and data access requirements in a SQL database.

Module 3: Write advanced T-SQL code

Students develop advanced T-SQL skills used in enterprise database solutions. This module covers complex query structures, window functions, JSON processing, pattern matching, graph queries, correlated subqueries, and structured error handling.

Topics include:

• Common table expressions, including recursive CTEs.
• Window functions for ranking, running totals, and analytic queries.
• JSON_VALUE, JSON_QUERY, and JSON transformation patterns.
• Regular expressions and fuzzy matching.
• Graph queries and MATCH traversal.
• Correlated subqueries and EXISTS patterns.
• TRY…CATCH, THROW, and transaction-safe error handling.
• Writing maintainable and performant advanced SQL code.

Lab: Write advanced T-SQL queries
Students practice advanced SQL techniques using AdventureWorksLT. They write queries using JSON functions, CTEs, window functions, and other advanced T-SQL patterns.

Module 4: Implement SQL solutions by using AI-assisted tools

Students learn how AI-assisted development tools can improve SQL development productivity while still requiring human review, security validation, and performance testing. The module introduces GitHub Copilot, Fabric Copilot, AI assistance in SQL tooling, custom instructions, and Model Context Protocol, or MCP, server connections.

Topics include:

• Using GitHub Copilot and Fabric Copilot for SQL development.
• Using AI assistance in SQL Server Management Studio and VS Code.
• Writing prompts for T-SQL generation, explanation, and optimization.
• Creating Copilot instruction files for coding standards.
• Understanding security and privacy considerations for AI-assisted development.
• Configuring model and MCP tool options.
• Connecting MCP servers to SQL Server, Azure SQL, and Fabric.
• Reviewing AI-generated SQL for correctness, performance, and compliance.

Lab: Implement SQL solutions by using AI-assisted tools
Students use GitHub Copilot in VS Code to accelerate T-SQL development against Azure SQL Database. They create custom instruction files, generate stored procedures and views, ask Copilot to explain code, and review AI-generated suggestions for optimization.

Learning Path 2: Secure, optimize, and deploy database solutions

Module 5: Implement data security and compliance with SQL

Students learn how to protect SQL data using encryption, masking, row-level security, permissions, authentication, auditing, and secure access to AI model and API endpoints.

Topics include:

• Transparent Data Encryption.
• Always Encrypted.
• Dynamic Data Masking.
• Row-Level Security.
• Role-based access control and object permissions.
• Authentication and identity options.
• SQL auditing for security and compliance.
• Securing AI model endpoints.
• Using managed identity instead of hardcoded secrets.
• Using sp_invoke_external_rest_endpoint securely.

Lab: Implement security and compliance with SQL
Students implement security features in Azure SQL Database. They apply Dynamic Data Masking to sensitive columns such as SSN, salary, email, credit card, and phone data. They also configure Row-Level Security with predicate functions to restrict data access by user region.

Module 6: Optimize database performance

Students learn how to evaluate, diagnose, and tune SQL database performance. They compare service tiers, isolation levels, query execution plans, Query Store, blocking, deadlocks, parameter sniffing, and indexing recommendations.

Topics include:

• Choosing Azure SQL database configurations.
• Understanding service tiers and performance requirements.
• Preserving data integrity with isolation levels.
• Reading query execution plans.
• Using dynamic management views.
• Using Query Store for regression analysis.
• Forcing execution plans when appropriate.
• Applying Query Store hints.
• Identifying missing indexes.
• Troubleshooting blocking and deadlocks.

Lab: Optimize query performance
Students investigate and resolve performance issues in Azure SQL Database using execution plans, DMVs, and Query Store. They identify missing indexes, simulate and fix parameter sniffing regression with plan forcing, apply Query Store hints, and diagnose blocking scenarios.

Module 7: Implement CI/CD by using SQL Database Projects

Students learn how to manage database schema changes as code using SQL Database Projects, .dacpac artifacts, Git, schema comparison, drift detection, testing, and deployment pipelines.

Topics include:

• SQL Database Projects.
• Declarative database-as-code.
• Building .dacpac artifacts.
• Using source control for database projects.
• Using pre-deployment and post-deployment scripts.
• Managing reference data.
• Branching and pull request workflows.
• Resolving SQL project conflicts.
• Detecting and resolving schema drift.
• Building CI/CD pipelines with GitHub Actions or Azure DevOps.
• Testing database changes before deployment.

Lab: Implement CI/CD with SQL Database Projects
Students create a SQL Database Project, build a .dacpac, and configure a database deployment workflow. They practice source-controlled schema changes and learn how SQL CI/CD differs from application CI/CD because databases are stateful.

Module 8: Integrate SQL solutions with Azure services

Students learn how to expose and integrate SQL data with Azure application services, APIs, monitoring, and event-driven patterns. The module focuses on Data API Builder, REST and GraphQL endpoints, Azure deployment options, monitoring, and data-change patterns.

Topics include:

• Data API Builder configuration.
• Exposing SQL tables, views, and stored procedures as APIs.
• REST and GraphQL endpoints.
• Secure connection-string management.
• Deploying Data API Builder to Azure Container Apps.
• Monitoring with Application Insights and Log Analytics.
• Using alerts and telemetry.
• Change Data Capture.
• Change Tracking.
• Change event streaming and event-driven synchronization.

Lab: Integrate SQL solutions with Azure services
Students configure Data API Builder to expose a product catalog database through REST and GraphQL endpoints. They secure configuration values, define entities, test API access, and prepare the solution for Azure deployment.

Learning Path 3: Implement AI capabilities in database solutions

Module 9: Design and implement models and embeddings with SQL

Students learn how AI models interact with SQL data and how embeddings are generated, stored, maintained, and searched in SQL databases. The module introduces external models, tokens, embedding dimensions, chunking strategies, vector columns, and managed identity-based access to AI endpoints.

Topics include:

• Understanding model types and evaluation criteria.
• How AI models interact with SQL data.
• Tokens and model input limits.
• Creating external model definitions.
• Using Azure OpenAI models from SQL.
• Designing embedding inputs and chunks.
• Choosing text columns for embedding generation.
• Creating VECTOR columns.
• Generating embeddings with SQL functions.
• Maintaining embeddings when source data changes.
• Using managed identity for model access.

Lab: Generate and update embeddings in Azure SQL Database
Students generate and store vector embeddings in Azure SQL Database using AI_GENERATE_EMBEDDINGS with an Azure OpenAI model. They configure managed identity, create external model references, generate embeddings in batches, validate results with vector search, and update embeddings when source data changes.

Module 10: Design and implement intelligent search with SQL

Students learn how to implement search patterns that combine traditional keyword search and modern vector search. They compare full-text search, vector search, semantic similarity search, hybrid search, and reciprocal rank fusion.

Topics include:

• Choosing a search approach.
• Full-text search with CONTAINS and FREETEXT.
• Full-text indexes and linguistic search.
• Inflectional forms and thesaurus search.
• Preparing SQL vector search.
• Designing vector columns with correct dimensions.
• Using vector distance functions.
• Using approximate nearest-neighbor indexes.
• Using DiskANN indexes.
• Combining full-text and vector search.
• Hybrid search with reciprocal rank fusion.
• Ranking and relevance scoring.

Lab: Implement intelligent search with full-text, vector, and hybrid queries
Students implement and compare full-text search, vector search, and hybrid search in Azure SQL Database. They use keyword predicates, vector similarity, DiskANN indexing, and reciprocal rank fusion to produce more relevant search results.

Module 11: Design and implement RAG with SQL

Students learn how to build retrieval-augmented generation solutions where SQL data provides grounding context for large language model responses. They design retrieval logic, prepare SQL results as JSON, augment prompts, call Azure OpenAI from SQL, and package RAG logic into reusable stored procedures.

Topics include:

• What RAG is and why it is used.
• When to use RAG with SQL.
• RAG compared with fine-tuning.
• Preparing retrieval context from SQL data.
• Converting SQL result sets to JSON.
• Building grounded prompts in T-SQL.
• Using database context to reduce hallucinations.
• Calling Azure OpenAI from SQL.
• Using sp_invoke_external_rest_endpoint.
• Processing model responses.
• Packaging RAG logic into stored procedures.
• Designing reusable RAG pipelines.

Lab: Implement RAG solutions with SQL
Students implement a complete RAG solution using Azure SQL Database and Azure OpenAI. They retrieve relevant reviews using vector search, build augmented prompts with JSON context, call the OpenAI endpoint from T-SQL, and package the workflow into a reusable stored procedure.

What hands-on labs and practice activities are included?

Hands-on activities vary by Microsoft course, but Dynamics Edge uses the official course themes to create practical exercises, demonstrations, review tasks, and implementation discussions.

• Design and implement database objects with SQL
• Implement programmability objects with SQL.
• Write advanced T-SQL queries
• Implement SQL solutions by using AI-assisted tools
• Implement security and compliance with SQL
•  Optimize query performance
• Implement CI/CD with SQL Database Projects
• Integrate SQL solutions with Azure services
• Generate and update embeddings in Azure SQL Database
• Implement intelligent search with full-text, vector, and hybrid queries
• Implement RAG solutions with SQL

How does DP-800T00 training support certification preparation?

This course supports Microsoft certification preparation by connecting course topics to the official study guide, instructor-led review, practice discussion, and job-task outcomes.

Who should attend DP-800T00 training?

This course is for professionals who need Microsoft skills for implementation, administration, development, analytics, security, AI, cloud operations, or business applications work.

• Database developers who build SQL Server, Azure SQL, or Microsoft Fabric SQL database solutions.
• Application developers who integrate SQL-based data services into modern applications.
• Data professionals who want to add AI features such as embeddings, vector search, and RAG to database solutions.
• DevSecOps engineers and database professionals involved in CI/CD, security, and deployment of SQL solutions.
• Candidates preparing for the DP-800: Develop AI-enabled database solutions certification exam.

What are the prerequisites for DP-800T00 training?

Prerequisites vary by Microsoft course level, but learners should understand the basic platform, product, or job role connected to the course. For advanced courses, prior hands-on experience with the related Microsoft technology is recommended.

• Writing T-SQL queries and developing database objects.
• SQL Server, Azure SQL Database, or SQL databases in Microsoft Fabric.
• Basic database design, indexing, stored procedures, functions, and views.
• GitHub-based CI/CD concepts, including branches, pull requests, and deployment pipelines.
• AI concepts such as models, embeddings, vectors, semantic search, and RAG.
• Basic Azure concepts, including identity, security, Azure Portal, command-line tools, and Azure services.

Can Dynamics Edge deliver private team or government DP-800T00 training?

Yes. Dynamics Edge can deliver this Microsoft course as public training, private team training, government training, onsite training by request, or a customized workshop aligned to your implementation or adoption plan.

• Private classes can emphasize your organization’s cloud environment, business applications, security requirements, data model, governance standards, or project timeline.
• Government and regulated teams can request role-based delivery with security, adoption, and operational readiness discussion.
• Dynamics Edge can combine this course with related Microsoft courses, workshops, or custom implementation training.

Frequently asked questions about DP-800T00 training

Is DP-800T00 an official Microsoft course?

Yes. The course page is based on official Microsoft Learn course topics and then rewritten into a Dynamics Edge training outline for readability, SEO, and buyer clarity.

Can this course be delivered privately?

Yes. Dynamics Edge can deliver private training for teams that need customized examples, project alignment, government delivery, or role-based Microsoft training.

Does this course help with certification?

If the course maps to a Microsoft exam or certification, Dynamics Edge can include certification review, study guide alignment, practice discussion, and exam preparation guidance.

Course Review

Before attending class, review the course modules and identify the topics most important to your job role, project, or certification plan. Bring questions about implementation, configuration, development, analytics, security, AI, governance, or operational readiness so the instructor can connect the course to real work.

Certification Exam Review

After class, use the course outline, certification page, study guide, and instructor review points to plan focused practice. Spend additional time on the highest-weighted exam areas if an official Microsoft exam skills map is available.

How do I register for DP-800T00 training?

Register for Dynamics Edge instructor-led Microsoft training to build practical skills for Develop AI-enabled database solutions, team readiness, implementation support, and certification preparation.