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AI-500: Architecture & Design Patterns Flashcards
Microsoft Multi-Agent AI Solutions Expert AI-500 Flashcards
Study our AI-500: Architecture & Design Patterns flashcards for the Microsoft Multi-Agent AI Solutions Expert AI-500 exam with 31+ flashcards. View as flashcards, a searchable table, or as a fun matching game.
| Front | Back |
| Actor model vs agent model difference | Actor model focuses on message passing encapsulated state Agent model adds goals planning and reasoning |
| Agent discovery and registry pattern | Use a service registry or metadata store for available agents and capabilities |
| Azure Functions use case | Serverless event driven compute for small stateless processing tasks |
| Azure OpenAI typical responsibilities | Text generation summarization embeddings and few shot prompting |
| Blackboard pattern summary | Shared knowledge base where agents read write and coordinate via common state |
| Broker pattern in agent systems | Broker mediates communication decouples clients and agents |
| Cognitive Search and vector search mapping | Use Cognitive Search with semantic ranking and vector store for retrieval augmented generation |
| Cosmos DB when to use | Use Cosmos DB for low latency globally distributed application state |
| Cost vs performance optimization ideas | Use model selection batching caching and autoscaling to balance cost and QoS |
| Designing for extensibility tips | Keep clear component boundaries version APIs and use feature toggles for rollout |
| Durable Functions role in orchestration | Durable Functions implement reliable long running orchestrations with state management |
| Embedding storage strategies | Store embeddings in vector DB like Cognitive Search or Cosmos DB with index |
| Event driven topology benefit | Loose coupling asynchronous scaling and resilience to partial failures |
| Event Hubs typical use case | Ingest telemetry and streaming data at high scale |
| Failure handling strategies | Retries exponential backoff dead letter queues and compensating actions |
| Fan out fan in pattern use case | Parallel tasks executed then aggregated for faster throughput |
| Latency vs consistency trade off | Stronger consistency can increase latency choose per user experience requirements |
| Mediator pattern role | Mediator centralizes complex interaction logic between agents |
| Monitoring and observability for agents | Collect telemetry traces logs and metrics with Azure Monitor and Application Insights |
| Multi agent architecture pattern definition | System of autonomous agents collaborating through messages to achieve goals |
| Orchestration vs autonomy key difference | Orchestration uses a central coordinator Autonomy lets agents act independently |
| Pipeline pattern for LLM workflows | Sequential components each performing a transformation on the request |
| Pub sub pattern on Azure | Use Event Grid or Service Bus for event distribution and decoupled consumers |
| Reliable messaging choice Service Bus vs Event Grid | Use Service Bus for ordered durable messages Event Grid for high throughput event routing |
| Security best practices on Azure for AI | Use Key Vault managed identities role based access and network isolation |
| State management options on Azure | Use Cosmos DB Durable Entities or Blob Storage depending on consistency and latency needs |
| Stateless vs stateful components trade off | Stateless scales easily but needs external stateful stores for context |
| Testing multi agent systems approach | Use integration tests contract tests and scenario based simulations |
| Throughput vs accuracy trade off | Higher throughput may require model quantization smaller models at cost of accuracy |
| When to choose autonomy | Use autonomy for resilience local decision making and decentralized scaling |
| When to choose orchestration | Use orchestration for strict workflows transactions and centralized control |
About the Flashcards
Preparing for your certification requires a solid understanding of complex system design and cloud integration. These Flashcards for the Microsoft Multi-Agent AI Solutions Expert exam provide a comprehensive review of essential terminology, concepts, and key ideas tested on the exam. Students can reinforce their knowledge of multi-agent architecture patterns, orchestration, and autonomy, while evaluating various design trade-offs like latency versus consistency.
Additionally, this deck helps you review practical implementations using event-driven topologies and reliable messaging. You will explore state management options, vector search integration, and operational best practices such as security, monitoring, and failure handling.
Topics covered in this flashcard deck:
- Multi agent architecture patterns
- Orchestration and autonomy strategies
- Event driven cloud services
- State management and storage options
- System optimization and design tradeoffs