Reviews 4,2
Reviews 4,5
DP-800 Develops AI-enabled database solutions
Modern software systems need databases that do more than store data – they need to support AI, automation, and analytics at scale. Building the right foundation means rethinking how databases are designed, secured, and optimized.
Key topics
- Building intelligent database structures with SQL
- Embedding AI services and data models effectively
- Implementing scalable data solutions on Azure
- Ensuring data quality and accelerating queries
- Optimizing storage and query processes systematically
- Implementing access security and authorization logic
- Structuring data for AI workflow delivery
Prerequisite
A solid understanding of SQL and hands-on experience with relational database systems are required. Familiarity with cloud environments is helpful but not mandatory.
Target group
Designed for professionals looking to advance their database architecture skills – particularly those working in cloud, data platforms, and AI integration.
The training builds technical confidence for data projects where AI, cloud capabilities, and performance requirements converge.
Course Content
Planning and Building Database Structures with SQL
- Exploring and assessing SQL Server-based platform options
- Creating well-structured and efficient tables
- Accelerating queries through targeted indexing
- Applying special table types where appropriate
- Enforcing data integrity with constraints
- Managing JSON data and associated indexes
- Partitioning tables for scalability
Developing and Using SQL Programming Objects
- Building and applying database views
- Creating and using stored procedures
- Defining scalar functions
- Implementing table-valued functions
- Using triggers effectively
- Determining the right option for each use case
Applying Advanced T-SQL Queries and Techniques
- Structuring queries with Common Table Expressions
- Using window functions for analytical evaluations
- Processing JSON data with built-in functions
- Matching patterns with regular expressions
- Finding approximate matches using fuzzy string methods
- Traversing relationships with graph queries
- Comparing rows using correlated subqueries
- Handling errors systematically with TRY...CATCH
Using AI-Powered Development Tools for SQL
- Getting to know AI-powered development tools available for Microsoft SQL platforms
- Evaluating the security impact of using AI-based tools
- Activating GitHub Copilot and Fabric Copilot
- Configuring model and MCP options in GitHub Copilot or Fabric Copilot sessions
- Creating and customizing GitHub Copilot instruction files
- Connecting to MCP server endpoints including Microsoft SQL Server and Fabric Lakehouse
Implementing SQL Security Concepts and Compliance
- Applying data encryption as a protective measure
- Setting up dynamic masking for sensitive data
- Implementing row-level security rules
- Controlling and securing access permissions
- Configuring audit logging and monitoring
- Setting up secure access to AI services
- Effectively securing data API endpoints
Analyzing and Improving Database Performance
- Recommending and implementing appropriate database configurations
- Ensuring data integrity through isolation levels and concurrency controls
- Analyzing query performance using execution plans and DMVs
- Monitoring and improving queries with Query Store and Performance Insight
- Identifying and resolving blocking and deadlock situations
Building CI/CD Pipelines for SQL Database Projects
- Creating, compiling, and validating SQL database projects
- Setting up source control and managing reference data
- Managing branches, pull requests, and conflict resolution
- Detecting and correcting schema discrepancies
- Setting up automated CI/CD pipelines
- Designing and implementing a testing strategy for database projects
Connecting and Deploying SQL with Azure Services
- Creating configuration files for the Data API Builder
- Defining REST and GraphQL entities
- Making database objects, stored procedures, and views accessible
- Evaluating deployment options for the Data API Builder
- Recommending Azure Monitor configurations
- Processing changes using event-driven patterns
Using AI Models and Embeddings for SQL Workloads
- Selecting and evaluating models for SQL workloads
- Creating and managing external AI models in SQL
- Developing embedding concepts for SQL database scenarios
- Generating, managing, and maintaining embeddings
Building Intelligent Search Capabilities with SQL
- Choosing the right search approach for each scenario
- Setting up and applying full-text search in SQL
- Preparing the SQL environment for vector search
- Implementing query patterns for vector search
- Building hybrid search and hybrid ranking
Developing RAG Architectures with SQL
- Analyzing RAG use cases and selecting the right architecture
- Preparing the retrieval context for prompt augmentation
- Enriching prompts with relevant database context
- Generating and processing RAG responses
Up to 100% funding for your continuing education
Attractive funding opportunities are available for numerous continuing education programs – depending on the funding program, up to 100% funding is possible.
The requirement is that you live or work in Germany. Our funding opportunities are not available outside of Germany.
Funding opportunities are available for career starters, job seekers, career changers, startups, experienced professionals, those seeking career advancement, and specialists.
What practical value does the DP-800 offer?
- The training delivers directly applicable knowledge for modern database development using AI, Azure services, and automated data processes.
What career paths does DP-800 open up?
- Highly relevant for roles in data engineering, cloud architecture, AI development, and data-driven software engineering.
What makes AI-capable databases so significant?
- They enable faster analysis, greater automation, and better decision-making in data-intensive organizations.
What core technologies does the training cover?
- The focus is on Azure SQL, data integration, AI models, and intelligent database architectures including search solutions.
What value does DP-800 offer to organizations?
- Highly relevant – especially for companies looking to scale data processes and leverage AI for better business decisions.
What can you implement after completing the training?
- Build high-performance, intelligent data solutions for reporting, forecasting, and automated processing workflows.
Why is now the right time for this training?
- Demand for professionals with AI and data platform expertise is growing noticeably across virtually all industries.
What disadvantages arise without this knowledge?
- Outdated data structures, inefficient processes, and missing AI integration weaken long-term competitiveness.
