AI World 2 · Grounded Answers · Stage 3

Pick the Store

OpenSearch Serverless, Aurora pgvector, Neptune Analytics or S3 Vectors: the store decides what a query can do and what it costs.

Vector stores
OpenSearch Serverless
Aurora pgvector
Neptune Analytics (GraphRAG)
S3 Vectors
Hybrid search

Domain: AI and machine learning
World: AI World 2: Grounded Answers (Associate)
Game type: Solution board: Drop services into each row's slots, then run records through them.
Time: About 25 minutes on a first attempt (estimated)
Exam guides: DEA-C01Skill 2.1.3: Apply storage services to appropriate use cases (for example, using indexing algorithms like Hierarchical Navigable Small Worlds [HNSW] with Amazon Aurora PostgreSQL and using Amazon MemoryDB for fast key/value pair access)
AIF-C01Task 3.1: Describe design considerations for applications that use foundation models (FMs)
MLA-C02Skill 1.1.7: Configure scalable vector databases for AI applications (for example, OpenSearch Service, Amazon RDS with pgvector, Amazon S3) based on specifications; Skill 2.1.5: Select Retrieval Augmented Generation (RAG) architecture patterns based on use case requirements; Skill 3.2.7: Create and manage Amazon Bedrock knowledge bases with vector database configurations, document indexing, and retrieval optimization
AIP-C01Skill 1.4.1: Create advanced vector database architectures specifically for FM augmentation to enable efficient semantic retrieval beyond traditional search capabilities (for example, by using Amazon Bedrock Knowledge Bases for hierarchical organization, Amazon OpenSearch Service with the Neural plugin for Amazon Bedrock integration for topic-based segmentation, Amazon RDS with Amazon S3 document repositories, Amazon DynamoDB with vector databases for metadata and embeddings); Skill 1.4.3: Implement high-performance vector database architectures to optimize semantic search performance at scale for FM retrieval (for example, by using OpenSearch sharding strategies, multi-index approaches for specialized domains, hierarchical indexing techniques); Skill 1.5.3: Deploy and configure vector search solutions to enable semantic search capabilities for FM augmentation (for example, by using OpenSearch Service with vector search capabilities, Amazon Aurora with the pgvector extension, Amazon Bedrock Knowledge Bases with managed vector store functionality)