Snowflake AI Engineer Case Interview – Designing a Production RAG and ML Inference Platform on the Data Cloud

What this case covers (Snowflake-specific): - Scenario: You are the AI engineer asked to design a customer-facing “Data Cloud Assistant” that answers enterprise questions over first‑party CRM/usage data already in Snowflake and a corpus of product docs in cloud storage. You must propose an end‑to‑end design that runs primarily inside Snowflake, balances latency/cost, and adheres to Snowflake’s governance model. - Core focus areas: (1) Data ingestion and transformation with Streams/Tasks, Dynamic Tables, and Snowpipe Streaming; (2) Document processing and embedding via Snowpark Python UDFs or Snowpark Container Services, and use of Snowflake Vector Search for retrieval; (3) RAG orchestration using Snowflake Cortex AI functions vs. hosting/fine‑tuning models in‑account; (4) Online inference patterns (batch vs. real‑time), feature/embedding stores, and model versioning/rollback using Time Travel and Zero‑Copy Cloning; (5) Security/governance with RBAC, object tagging, dynamic data masking, row/column‑level policies, Access History auditing, and cross‑account Secure Data Sharing; (6) Cost/performance: warehouse sizing vs. serverless choices, result/metadata caches, micro‑partitioning, clustering, multi‑cluster scaling, and Resource Monitors; (7) Reliability/SRE: monitoring (Query/Task history), canary releases, A/B evaluation, incident playbooks, and multi‑region replication/failover. - Expected deliverable in the interview: A whiteboard/system design that names specific Snowflake primitives, a request/response flow for RAG (query → retrieval → grounding → generation → post‑processing), and a justification of build vs. buy decisions (e.g., Cortex AI vs. external models) with latency, throughput, and cost estimates. Include how you’d evaluate answer quality (offline eval sets, human‑in‑the‑loop, guardrails) and handle PII/regulated data. - Typical prompts and drill‑downs (based on real Snowflake interview patterns): • Ingestion: Show how new docs land via an external stage and are processed into embeddings incrementally (Streams + Tasks). Explain backfills and schema evolution. • Retrieval: Compare Vector Search vs. keyword fallback; discuss chunking, re‑ranking, and cold‑start. Outline how you’d tune recall/precision and control hallucinations with grounding. • Inference: Choose between Cortex AI serverless calls and hosting a custom model with Snowpark Container Services; provide a latency budget and scaling plan for 1–2k QPS. • Governance: Apply dynamic masking to PII, enforce row‑level policies for multi‑tenant access, and audit with Access History. Describe cross‑account sharing for customers while preventing data exfiltration. • Ops & cost: Propose warehouse sizes, auto‑suspend settings, and Resource Monitors. Show how Query Profile informs performance fixes. Plan zero‑downtime rollouts using Zero‑Copy Clones and Time Travel. - What interviewers assess (aligned with Snowflake culture: customer‑first, pragmatic excellence, ownership, clarity): Depth on Snowflake internals; ability to make and communicate trade‑offs; security and cost consciousness; measurable evaluation strategy; clear written/diagrammed thinking; and willingness to say “it depends” with concrete criteria. - You may be asked to sketch lightweight SQL/Python (e.g., a Snowpark UDF signature, a Vector Search query, or a Streams/Tasks DAG), but the emphasis is systems design and decision rationale rather than long coding.