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AI-500: Data, Knowledge & Memory Management Flashcards
Microsoft Multi-Agent AI Solutions Expert AI-500 Flashcards
Study our AI-500: Data, Knowledge & Memory Management flashcards for the Microsoft Multi-Agent AI Solutions Expert AI-500 exam with 46+ flashcards. View as flashcards, a searchable table, or as a fun matching game.
| Front | Back |
| Define retrieval augmented generation RAG | A technique that retrieves relevant documents then conditions generation on those documents to improve accuracy |
| Explain cosine similarity versus dot product | Cosine similarity measures angle similarity normalized by magnitude dot product measures raw magnitude overlap |
| Explain vector dimensionality trade off | Higher dimensions can capture nuance but increase storage and computation costs |
| How are embeddings generated | By passing text or data through a trained model that outputs fixed length vectors |
| How do embeddings integrate with Cognitive Search | Upload embeddings to a vector capable index to enable semantic similarity search |
| How to build a retrieval pipeline | Ingest clean data chunk text generate embeddings index vectors and implement similarity search then rank results |
| How to choose chunk size | Balance context preservation with embedding capacity often between 200 and 1000 tokens depending on model |
| How to evaluate retrieval quality | Use relevance metrics user satisfaction human evaluation and downstream task performance |
| How to handle multi turn context windows | Use truncation prioritization summarization and hierarchical state to fit context within token limits |
| How to handle sensitive data in embeddings | Apply differential privacy anonymization encryption and strict access controls |
| How to implement feedback loops for memory systems | Capture user corrections validate them and update memory with review processes |
| How to reduce token usage for context management | Use summarization selective recall and retrieval of only relevant chunks |
| How to secure knowledge bases | Use access controls encryption auditing and input sanitization |
| List benefits of retrieval augmented generation | Improves factual accuracy provides source grounding enables up to date responses |
| Name long term memory strategies | Selective storage summarization periodic review retrieval indexing |
| Name mitigation techniques for hallucinations | Use retrieval grounding chain of thought verification and constrained decoding |
| What are ANN algorithms | Approximate Nearest Neighbor algorithms that speed up similarity search with sublinear performance |
| What are best practices for indexing large corpora | Use incremental ingestion sharding batching and monitoring for index health |
| What are common challenges of RAG | Retrieval errors outdated sources context misalignment and hallucinations |
| What are embeddings | Numerical vector representations that capture semantic meaning of text or other modalities |
| What are knowledge graphs | Graph structures that represent entities and relationships to enable reasoning and rich queries |
| What are structured semi structured and unstructured data | Structured data follows a schema semi structured has tags or keys and unstructured is freeform like text or images |
| What is a knowledge base | A curated repository of facts documents and relations used to support retrieval and reasoning |
| What is a vector store | A database optimized for storing embeddings and performing similarity search |
| What is Azure Cognitive Search | A managed search service that provides indexing semantic search and enriched document ingestion |
| What is Azure Cosmos DB | A globally distributed multi model database service for low latency and high availability |
| What is chunking in document processing | Splitting large documents into smaller chunks for embedding and retrieval |
| What is context management in conversational AI | Managing the information that determines conversation state and influences model responses across turns |
| What is data modeling in AI systems | The process of structuring and organizing data for AI applications using schemas types and relationships |
| What is embedding drift | When embeddings for similar content change over time due to model updates or data shifts leading to inconsistency |
| What is Faiss | An open source library by Facebook for efficient similarity search and clustering of dense vectors |
| What is fine tuning versus retrieval | Fine tuning updates model weights retrieval supplies external relevant content without changing model weights |
| What is hallucination in LLMs | When a model generates plausible but incorrect or fabricated information |
| What is hybrid search | Combining keyword based filtering with vector based semantic similarity to improve relevance |
| What is index latency | Time taken to update or query an index affecting freshness and responsiveness |
| What is long term memory in AI agents | Persistent storage of user preferences facts and events that the agent can recall across sessions |
| What is Milvus | An open source vector database designed for scalable similarity search |
| What is semantic retrieval | Retrieving items based on meaning rather than exact keyword overlap |
| What is session affinity | A technique to route requests from the same user to the same backend to preserve session state |
| What is session state | Transient data that tracks user interactions and context within a conversation session |
| What is the cold start problem in memory systems | When there is insufficient stored data to make effective personalized or retrieval based decisions |
| What is vector quantization | A technique to compress vectors by approximating them with limited codebooks to reduce storage |
| What is versioning for knowledge bases | Tracking updates and history of documents indexes and schemas to enable rollbacks and audits |
| What metrics are used for vector search | Recall at K precision at K mean reciprocal rank and normalized discounted cumulative gain |
| When use vector search versus keyword search | Use vector search for semantic similarity and keyword search for exact matches and boolean logic |
| Why use Cosmos DB for AI workloads | Supports scalable storage fast reads global distribution and multiple data models |
About the Flashcards
Flashcards for the Microsoft Multi-Agent AI Solutions Expert exam help students review core AI data concepts, including data modeling, structured and unstructured data, knowledge bases, and knowledge graphs. The deck emphasizes how information is organized, stored, retrieved, and used to support AI applications.
Students can practice terminology and key ideas related to retrieval augmented generation, embeddings, vector stores, semantic search, and context management. Cards also cover memory systems, document chunking, indexing, retrieval quality metrics, hallucination mitigation, and security considerations for knowledge bases and embeddings.
Topics covered in this flashcard deck:
- AI data modeling
- RAG and retrieval
- Embeddings and vectors
- Context and memory
- Search and indexing
- Knowledge base security