Vector Database Interview Questions PDF

Vector databases power every RAG system, yet most candidates can't explain ANN algorithms or hybrid search. This fundamental question appears in 80% of AI engineering loops. Master dense vs sparse retrieval and when hybrid search wins.

Medium vector DB / embeddings trade-off question on dimensions vs cost/quality.

Hard vector DB question on tenant isolation, noisy neighbors, and ACL safety.

Latency, recall proxies, ingest lag, index size, filter rates, error budgets, and RAG downstream signals.

Pricing dimensions: vectors stored, dimensions, QPS, namespaces, egress, and when self-host breaks even.

PostgreSQL + pgvector tuning: HNSW vs IVFFlat, connection pooling, vacuum, partitioning, and when to outgrow Postgres.

Snapshot strategies, embedding model lineage, cross-region restore, and rebuild-from-source playbooks.

Read replicas, eventual consistency after upserts, quorum writes, and RAG staleness expectations.

500M vectors at sub-100ms p99 is a staff-level vector search design question from Uber, Airbnb, and large-scale ML platform teams. Learn sharding strategies, index tuning, and the operational trade-offs that separate senior from principal engineers.

Latency, availability, freshness, recall, and error budget definitions with realistic dependencies.

Field selection, normalization, ACL tags, temporal fields, and schema evolution without breaking filters.

Brute-force exact search thresholds, ANN recall trade-offs, and hybrid exact re-rank on shortlists.

Company-based system design: sharded ANN, metadata pre-filtering, query routing, reranking, and latency SLOs at marketplace scale.

Encryption at rest/transit, tenant isolation, ACL on metadata filters, PII in embeddings, and audit logging.

Write buffering, mutable segments, async merge, backpressure, and consistency windows for hot ingestion paths.

Learned sparse representations, inverted index integration, lexical expansion, and fusion with dense ANN.

efSearch, nprobe, over-fetch for filters, offline benchmarks, and continuous monitoring of retrieval quality.

Shared embedding spaces, separate indexes, CLIP-style models, metadata routing, and fusion strategies.

Dual-write, shadow traffic, embedding compatibility, cutover rollback, and validation gates.

BM25 + vector fusion, RRF, weighted scores, rerankers, and same-chunk alignment pitfalls.

Bootstrap strategies, fallback retrieval, hybrid search defaults, and UX for new tenants or collections.

PCA, Matryoshka embeddings, learned compression, and recall impact when shrinking vector size.

Horizontal scaling patterns: hash sharding, semantic partitions, routing embeddings, and merge/rerank at query time.

Hard vector DB topic: filter-aware ANN, post-filter recall collapse, and hybrid query planning for tenant isolation.