Intermediate~3h
Vector Databases in RAG & Retrieval SystemsInterview Readiness Check
pgvector, Pinecone, Weaviate, Qdrant, Chroma — what differentiates them architecturally, how HNSW and IVF indexes work, when to use managed vs embedded, the latency vs recall trade-off..
vector databasepgvectorPineconeWeaviateQdrantChromaHNSWIVFANNapproximate nearest neighbor
Learning path
RAG Fundamentals→Vector Databases ← you are here→Dense RetrievalEmbedding Model Selection & TuningRAG Cost & Latency Optimization
Try a sample question
Your team is adding semantic search to a PostgreSQL-backed SaaS app. You want to avoid managing a separate vector database infrastructure. Which option lets you store and query embeddings closest to your existing data?
A.pgvector extension: adds a vector column type and ANN index directly in PostgreSQL
B.Pinecone: a fully managed vector database that connects to PostgreSQL via CDC
C.Redis with RediSearch: stores embeddings as hashes alongside relational data
D.Elasticsearch with dense_vector fields: integrates natively with PostgreSQL
Sign up free to take the full check and see the answer with an explanation.
What this module tests
- Conceptual understanding of vector databases fundamentals
- Trade-offs and design decisions in real system scenarios
- vector database and pgvector — applied knowledge
- Interview-style reasoning and explanation quality
Free to start
Take the Vector Databases check →
10 questions · Dimension scoring · AI coach feedback · Shareable result
Test your knowledge — free →No credit card · Takes 10–15 min
Frequently asked questions
- What does the Vector Databases check test?
- The check covers pgvector, pinecone, weaviate, qdrant, chroma — what differentiates them architecturally, how hnsw and ivf indexes work, when to use managed vs embedded, the latency vs recall trade-off.. Questions span beginner to advanced difficulty and test both conceptual understanding and practical trade-off reasoning.
- What keywords should I know for Vector Databases?
- Key concepts include: vector database, pgvector, Pinecone, Weaviate, Qdrant, Chroma, HNSW, IVF, ANN, approximate nearest neighbor. These frequently appear in rag & retrieval systems interviews at product and infrastructure companies.
- How many questions are in the check?
- Each check has 10 questions — a mix of MCQ and short-answer. You get scored by dimension (e.g., scalability, consistency) so you know exactly where to focus.
- How long does it take to study Vector Databases?
- Most engineers spend around 3 hours on this module. The check itself takes about 10–15 minutes. You can retake it up to 3 times per day.
- What prerequisite knowledge do I need before Vector Databases?
- Vector Databases builds on RAG Fundamentals. Complete those first for the best results.
- Is vector database, pgvector, Pinecone covered?
- Yes. The Vector Databases module covers vector database, pgvector, Pinecone among other key concepts. The check includes questions that reflect real rag & retrieval systems interview patterns.