DP-800 Develops AI-enabled database solutions

Price
Net €
VAT €

Price
Price on Request

Duration
4 days

For companies and job seekers:
this course is 100% fundable!
 

Location

Course Language
English

Training Solutions
Online Live

Request a consultation Add to favorites Print as PDF

The future of modern software lies in intelligent, data-driven applications. This requires database solutions that go far beyond traditional storage and are specifically designed for AI, automation, and scalable analytics.

Key Topics

  • Development of smart database architectures
  • Integration of AI services and data models
  • Cloud data solutions with Azure technologies
  • Data quality and performance
  • Query and storage optimization
  • Security and authorization concepts
  • Data provisioning for AI workflows

Prerequisites
Experience with relational databases and SQL; ideally, basic knowledge of cloud platforms.

Target Audience
Ideal for data engineers, database developers, cloud specialists, and professionals in the AI field.

Those who want to take a forward-thinking approach to data-intensive systems will strengthen skills that are increasingly in demand in modern IT, cloud, and AI projects.

Print as PDF
Course Content
  • Understanding SQL Server-based platform options
  • Creating Efficient Tables
  • Optimizing with Indexes
  • Using Special Table Types
  • Ensuring data integrity with constraints
  • Managing JSON columns and indexes
  • Partition tables for scaling
  • Create Views
  • Create stored procedures
  • Create scalar functions
  • Create table-valued functions
  • Create triggers
  • Determine when to use each option
  • Organizing Queries with Common Table Expressions
  • Using window functions for analysis
  • Processing JSON data with built-in functions
  • Matching patterns with regular expressions
  • Finding approximate matches with fuzzy string functions
  • Traversing relationships with graph queries
  • Comparing rows with correlated subqueries
  • Handling errors with TRY...CATCH
  • Description of the AI-powered development tools available for Microsoft SQL platforms.
  • Assessment of the impact of using AI-powered tools on security.
  • Enabling GitHub Copilot and Fabric Copilot.
  • Configuring model and MCP (Model Context Protocol) tool options in a GitHub Copilot or Fabric Copilot chat session.
  • Creating and configuring GitHub Copilot instruction files.
  • Connecting to MCP server endpoints, including Microsoft SQL Server and Fabric Lakehouse.
  • Protect data through encryption
  • Configure dynamic data masking
  • Implement row-level security
  • Manage permissions and secure access
  • Implement logging
  • Configure secure access to AI services
  • Secure API endpoints for data
  • Recommendations for Database Configurations
  • Ensuring data integrity through transaction isolation levels and concurrency controls
  • Evaluating query performance using execution plans and DMVs
  • Monitoring and optimizing queries using Query Store and Query Performance Insight
  • Detecting and resolving deadlocks
  • Creating, Building, and Validating SQL Database Projects
  • Configuring source code management and managing reference data
  • Managing branches, pull requests, and conflict resolution
  • Identifying and resolving schema discrepancies
  • Implementing CI/CD pipelines
  • Designing and implementing a testing strategy
  • Create configuration files for the Data API Builder
  • Define entities for REST and GraphQL
  • Make database objects, stored procedures, and views available
  • Explore deployment options for the Data API Builder
  • Recommend configurations for Azure Monitor
  • Process changes using event-driven patterns
  • Understanding and Evaluating Models for SQL Database Workloads
  • Create and manage external models in SQL
  • Designing embeddings for SQL database workloads
  • Generate and maintain embeddings for SQL database workloads
  • Selecting an intelligent search approach
  • Implementing full-text search
  • Preparing SQL for vector search
  • Implementation of query patterns for vector search
  • Implementation of hybrid search and hybrid ranking
  • Identification of RAG use cases and architecture
  • Preparing the retrieval context for the extension
  • Extending prompts with database context
  • Generation and processing of RAG responses

Frequently Asked Questions

  • Provides practical, immediately applicable knowledge for modern database solutions using AI, Azure, and automated data processes.
  • Particularly relevant for roles in data engineering, cloud solutions, AI development, and data-driven software development.
  • They improve analysis speed, automation, and decision-making quality in data-intensive companies.
  • The focus is on Azure SQL, data integration, AI models, and intelligent data architectures.
  • Yes, especially for organizations that want to scale their processes and leverage data economically using AI.
  • Development of high-performance, intelligent data solutions for reporting, forecasting, and automated workflows.
  • Demand for expertise in AI and data platforms is growing rapidly across all industries.
  • Outdated data structures, inefficient processes, and a lack of AI integration can lead to competitive disadvantages.
Berlin
Bremen
Dortmund
Dusseldorf
Frankfurt
Hamburg
Hanover
Cologne
Leipzig
Munich
Nuremberg
Stuttgart

Do you have any further questions? Please contact us.