Store Management (Feature/Knowledge) Test

Evaluates the end-to-end lifecycle of feature data — from definition, ingestion, transformation, validation, and materialization to feature deprecation. Focus areas include schema evolution, feature freshness and TTL policies, feature registry updates, and data drift handling. Candidates are assessed on maintaining training-serving consistency, automating materialization jobs, and understanding the operational differences between offline batch ingestion and online streaming feature updates.

Tests expertise in designing and managing vector-based knowledge systems that power LLM retrieval pipelines. Includes understanding embedding model selection, vectorization workflows, and similarity search techniques such as cosine similarity, FAISS indexing, and HNSW graphs. Candidates must demonstrate knowledge of how vector databases (Chroma, Pinecone, Weaviate) handle metadata filtering, persistence, and hybrid retrieval (vector + keyword search). The topic also explores embedding lifecycle, semantic relevance tuning, and index maintenance for performance optimization.

Focuses on the design, automation, and orchestration of feature ingestion pipelines. Assesses familiarity with ETL/ELT architecture, data transformation logic, and scheduling tools such as Apache Airflow, Databricks Workflows, or Prefect. Candidates are tested on streaming vs batch feature ingestion, dependency tracking, and data validation within CI/CD frameworks. Hard-level questions involve event-driven pipelines, real-time feature ingestion with Kafka/Kinesis, and fault-tolerant job orchestration strategies.

Examines the integration of feature and knowledge stores into model training, deployment, and retrieval workflows. Covers feature lookup APIs, feature transformation consistency across environments, and feature-to-model lineage tracking. In the context of LLMs, assesses the ability to connect LangChain/LlamaIndex pipelines to vector stores for Retrieval-Augmented Generation (RAG) and context-aware prompt enrichment. Candidates must also understand feature reuse in production ML systems and cross-model feature dependencies.

Tests the ability to design and implement observability layers for monitoring feature quality, freshness, and drift in real-time. Candidates must demonstrate knowledge of tools like Evidently AI, Great Expectations, and MLflow tracking to monitor data consistency, schema violations, feature staleness, and concept drift. The topic also covers monitoring retrieval performance in knowledge stores — including recall precision, embedding degradation, and retriever evaluation using metrics such as MRR and nDCG.

Evaluates understanding of data governance frameworks, access control mechanisms, and auditability in feature and knowledge store systems. Focus includes lineage tracking, RBAC, encryption standards, and PII/PHI data compliance (GDPR, HIPAA, SOC2). Hard-level questions involve defining feature retention policies, automated compliance enforcement, and integrating governance metadata into feature registries or data catalogs (e.g., Amundsen, DataHub). Candidates must understand how to achieve Responsible AI through traceable and explainable feature usage.

Measures proficiency in integrating graph databases (Neo4j, AWS Neptune, CosmosDB) into knowledge systems to capture relationships between entities, documents, or embeddings. Focus areas include knowledge graph schema design, entity resolution, and semantic query execution using Cypher/Gremlin/SPARQL. Hard questions involve hybrid retrieval systems combining vector search with graph traversal for contextual augmentation in LLMs, and building GraphRAG architectures that unify structured and unstructured knowledge retrieval.

Tests the candidate’s ability to optimize feature/knowledge stores for latency, throughput, and cost efficiency. Covers indexing strategies, caching mechanisms, partitioning schemes, load balancing, and query parallelization for high-performance retrieval. Medium questions assess optimization at the data layer, while hard questions focus on scale-out design, replication, vector quantization, and feature caching strategies for low-latency inference. Candidates should understand how to benchmark and tune vector retrieval systems at scale.

The capstone topic that assesses strategic and architectural competence in defining and governing enterprise-grade feature/knowledge management ecosystems. Focuses on designing multi-tenant, cross-domain feature stores, standardizing naming conventions and lifecycle policies, and establishing centralized discovery portals. Includes leadership-level awareness of FeatureOps, Featureform, Tecton, LangGraph, and DeepLake. Hard questions cover federated feature sharing, data mesh alignment, compliance-aware retrieval, and Responsible AI governance integrated into organizational data strategy.