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Build AI Applications with Azure Cosmos DB (DP-420T00-A)
- Categories:
- Data and Office Applications Software and DevOps
- Level:
- Intermediate
- Tier type:
- PerformancePlus
- Code:
- MDP420
- Exam:
- Yes - Available separately
Overview
This four-day intermediate course equips learners with the skills to design, develop, and optimize high-performance AI applications using Azure Cosmos DB for NoSQL. Participants gain practical experience working with Azure Cosmos DB, its NoSQL API, and software development kits, learning to create and manage database resources, connect applications, perform data operations, and develop effective data models and partitioning strategies. The course explores integrating AI capabilities such as vector search, full-text search, hybrid search, retrieval-augmented generation, and agent memory into database solutions. Learners also cover securing, monitoring, and optimizing database solutions, managing multi-region availability, and using Microsoft Fabric for operational analytics. Through hands-on exercises, delegates develop the ability to build scalable, secure, and reliable AI applications that leverage organizational data.
Prerequisites
- Experience developing cloud applications using C#, Python, Java, or JavaScript
- Experience working with SQL or NoSQL database platforms
- Basic familiarity with JSON and the Azure portal
- Understanding of fundamental cloud computing and application development concepts
- Familiarity with Python, vector embeddings, AI model integration, and Microsoft Azure services is beneficial for AI-focused modules
Target Audience
- Software developers building cloud-native applications using Azure Cosmos DB
- Application developers working with NoSQL databases and cloud-based data solutions
- AI engineers developing applications that use retrieval and generative AI capabilities
- Technical professionals responsible for designing scalable database solutions
- Developers seeking to expand their skills in AI application development and Azure data technologies
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4 Days instructor led course
Exam: Yes - Available separately
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Outline
Design and Develop Azure Cosmos DB Database Solutions
1. Getting Started with Azure Cosmos DB for NoSQL
- Explore the foundations of Azure Cosmos DB for NoSQL and develop skills to create and manage database solutions for cloud-native and AI applications
- Understand Azure Cosmos DB for NoSQL and its architecture
- Evaluate Azure Cosmos DB for different application workloads, including AI applications
- Explore database resources, containers, items, and fundamental operations
- Understand the structure and purpose of Azure Cosmos DB for NoSQL
- Evaluate its suitability for various application workloads
- Explore the process of creating accounts, databases, and containers
- Learn about fundamental operations on database resources
- Create an Azure Cosmos DB account, database, and container
2. Configuring Resources, Throughput, and Consistency
- Understand request units and assess workload throughput requirements
- Compare provisioned and serverless capacity models
- Configure standard and autoscale throughput
- Manage database-level and container-level throughput
- Evaluate consistency levels and their impact on application behavior
- Configure time to live for data retention
- Balance performance, scalability, and cost through resource configuration
3. Connecting Applications Using Software Development Kits
- Understand and configure Azure Cosmos DB software development kits
- Establish application connections using a client singleton
- Configure connectivity modes and connection settings
- Use the Azure Cosmos DB emulator for offline development
- Handle connection errors, logging, and parallel application operations
4. Implementing Database Operations
- Perform point read operations
- Create, replace, upsert, and delete database items
- Update documents using partial patch operations
- Configure item time to live
- Implement optimistic concurrency control
- Execute transactional batch operations
- Process bulk operations using the software development kits
5. Querying Data in Azure Cosmos DB for NoSQL
- Understand the SQL query language for Azure Cosmos DB
- Create and execute database queries
- Use parameters, projections, and built-in functions
- Implement correlated subqueries and cross-product queries
- Retrieve large result sets using continuation tokens
- Develop efficient querying techniques using C# and Python
6. Designing Data Models and Partitioning Strategies
- Identify application access patterns and aggregate boundaries
- Map aggregates to database items using embedded and referenced data
- Apply denormalization and pre-aggregation techniques
- Select appropriate partition keys
- Implement hierarchical partition keys
- Use synthetic keys and multi-tenant partitioning
- Identify partitioning anti-patterns and estimate workload costs
7. Processing Change Feeds
- Understand the Azure Cosmos DB change feed
- Implement change feed consumers using processor and pull models
- Select appropriate change feed modes
- Process database events using Azure Functions
- Move data using container copy jobs
8. Implementing AI-Assisted Development
- Explore AI-assisted development tools for Azure Cosmos DB
- Connect AI assistants to database resources using the Model Context Protocol server
- Explore agent toolkits, durable memory, and multi-step retrieval
- Evaluate the capabilities and appropriate use of AI-assisted development tools
- Choose the right tool and understand its scope
Secure, Optimize, and Deploy Azure Cosmos DB Solutions
9. Securing Azure Cosmos DB Solutions
- Implement Microsoft Entra authentication
- Configure key-based authentication and key rotation
- Understand control-plane and data-plane access
- Implement role-based access control
- Configure network security, private endpoints, service endpoints, and firewall rules
- Apply dynamic data masking to protect sensitive information
10. Configuring Backup, Recovery, and Availability
- Compare periodic and continuous backup options
- Configure continuous backup
- Perform point-in-time recovery
- Explore backup options for long-term retention
- Configure multi-region database availability
- Route application reads and writes across regions
- Manage write conflicts and multi-region configurations
- Configure account-level and partition-level failover
11. Designing and Optimizing Indexing Strategies
- Understand indexing policies and their impact on query performance
- Configure indexing policies for different workloads
- Implement composite and spatial indexes
- Configure computed properties
- Use global secondary indexes to improve query efficiency
12. Analyzing and Improving Query Performance
- Evaluate request unit consumption and identify high-cost queries
- Analyze query performance metrics
- Interpret index utilization metrics
- Optimize cross-partition queries
- Identify inefficient queries and apply appropriate performance improvements
13. Monitoring and Troubleshooting Database Solutions
- Interpret database response codes and diagnose throttling
- Monitor application latency and partition utilization
- Configure Azure Monitor alerts
- Analyze diagnostic logs using Log Analytics
- Investigate database performance issues and implement corrective actions
14. Managing Azure Cosmos DB at Scale
- Understand Azure Cosmos DB fleets and their management capabilities
- Organize multiple database accounts into fleets
- Configure fleet-level throughput management
- Use throughput pools to manage resource consumption
- Monitor database environments using fleet analytics
- Evaluate usage and cost across multiple accounts
Implement AI and Analytics Capabilities in Azure Cosmos DB
15. Implementing Full-Text and Vector Search
- Understand keyword-based and semantic retrieval
- Configure full-text search policies
- Implement vector search policies and indexes
- Evaluate vector index types and their limitations
- Generate and store vector embeddings
- Apply BM25 ranking and vector similarity
- Implement keyword and semantic searches against application data
16. Implementing Hybrid Search and Optimizing AI Retrieval
- Combine keyword and semantic search using hybrid retrieval
- Implement Reciprocal Rank Fusion to combine search results
- Optimize retrieval relevance, request unit consumption, and latency
- Evaluate search filters and query design
- Separate search workloads from transactional database operations
- Assess retrieval quality using consistent evaluation data
17. Building RAG Applications with Azure Cosmos DB
- Understand retrieval-augmented generation architecture
- Prepare and structure data for effective retrieval
- Design retrieval units and manage embedded content
- Integrate retrieved information with language models
- Construct prompts using verifiable source citations
- Implement evidence-based responses and appropriate handling of insufficient information
- Build retrieval pipelines that support follow-up questions
18. Designing and Implementing Agent Memory
- Understand the role of persistent memory in AI applications
- Design agent memory stores using Azure Cosmos DB
- Manage short-term conversation state and long-term memory
- Convert conversation data into meaningful, retrievable information
- Implement semantic memory retrieval
- Configure memory retention, reconciliation, and erasure policies
19. Implementing Operational Analytics with Microsoft Fabric
- Understand operational analytics using Azure Cosmos DB and Microsoft Fabric
- Configure continuous data mirroring into Microsoft Fabric OneLake
- Verify the integrity and reliability of mirrored data
- Query operational data using T-SQL and Spark
- Explore analytical access to database information without affecting transactional workloads
Exams and Assessments
- This course includes knowledge checks and module assessments to reinforce understanding and measure progress throughout the learning experience.
- Practical exercises provide opportunities to demonstrate the application of database configuration, software development kits, data modelling, security, performance optimization, AI retrieval, and analytics concepts.
- The Microsoft Certified: Azure Cosmos DB AI Developer Associate certification is identified as related to this course, but a formal certification examination is not specified as included.
Hands-on Learning
- Apply your knowledge through practical exercises using Azure Cosmos DB and associated Microsoft technologies.
- Activities include creating and configuring database resources, connecting applications, implementing data operations, designing partitioning strategies, securing database environments, and troubleshooting performance issues.
- Learners also work with vector search, AI-assisted development tools, retrieval-augmented generation applications, agent memory, and Microsoft Fabric analytics to develop practical experience of building AI-enabled database solutions.
Learning outcomes
Delegates will learn how to:
- Evaluate Azure Cosmos DB for NoSQL against application requirements and configure database resources, throughput, and consistency settings
- Connect applications to Azure Cosmos DB using software development kits and implement efficient data operations and queries
- Design data models and partitioning strategies that support application access patterns, scalability, and performance
- Implement change feed processing and use AI-assisted development tools to support application development
- Secure Azure Cosmos DB solutions using authentication, authorization, network controls, and data masking
- Configure backup, recovery, multi-region availability, and monitoring to maintain reliable database solutions
- Implement AI retrieval, vector search, hybrid search, retrieval-augmented generation, agent memory, and operational analytics capabilities
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