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IndiaQ1. Explain Snowflake’s architecture.
Short answer: Separated storage, compute (virtual warehouses) and cloud services layers.
Strong answer: Snowflake separates storage (centralized, compressed columnar data), compute (independent virtual warehouses that scale separately), and cloud services (metadata, optimization, security). This lets multiple warehouses query the same data without contention and scale compute independently of storage — the core of its elasticity and concurrency model.
Likely follow-up: Why does separating storage and compute matter?
What the interviewer is checking: Platform fundamentals.
Common mistake: Describing it like a traditional coupled database.
Q2. What is a virtual warehouse and how do you size it?
Short answer: A compute cluster for queries; size by workload and use auto-suspend to control cost.
Strong answer: A virtual warehouse is an independent MPP compute cluster. Size (XS→4XL) by query complexity and concurrency, scale up for heavier queries and out (multi-cluster) for concurrency. Use auto-suspend/auto-resume so you don’t pay for idle compute. Separate warehouses per workload avoid contention.
Likely follow-up: How does auto-suspend save money?
What the interviewer is checking: Cost/performance tuning.
Common mistake: Running one always-on oversized warehouse for everything.
Q3. How does Snowflake handle performance without manual indexes?
Short answer: Micro-partitions and automatic clustering/pruning replace traditional indexes.
Strong answer: Snowflake stores data in immutable micro-partitions with metadata (min/max) enabling automatic pruning — it scans only relevant partitions. There are no manual indexes; you influence pruning via natural or defined clustering keys on large tables. Good clustering plus result caching drives performance.
Likely follow-up: When would you define a clustering key?
What the interviewer is checking: Snowflake performance model.
Common mistake: Looking for traditional indexes to tune.
Q4. What are zero-copy cloning and Time Travel?
Short answer: Cloning creates instant metadata copies; Time Travel queries historical data.
Strong answer: Zero-copy cloning instantly creates a clone of a table/schema/database by copying metadata, not data, so dev/test copies are cheap. Time Travel lets you query or restore data as of a past point (up to the retention period), enabling recovery from mistakes. Both leverage Snowflake’s immutable storage.
Likely follow-up: How would you use cloning for a safe test environment?
What the interviewer is checking: Feature knowledge.
Common mistake: Physically duplicating data for test copies.
Q5. How do you load and transform data in Snowflake?
Short answer: Stage and COPY INTO for loading; Streams/Tasks and dbt for transformation (ELT).
Strong answer: Load via stages and COPY INTO (or Snowpipe for continuous ingestion), then transform in-warehouse (ELT) using SQL, Streams (change tracking) and Tasks (scheduling), often orchestrated with dbt. This leverages Snowflake compute for scalable transformation instead of external ETL.
Likely follow-up: What is Snowpipe used for?
What the interviewer is checking: Data-loading practice.
Common mistake: Transforming heavily outside before loading when ELT would scale better.
Q6. How do you control costs in Snowflake?
Short answer: Right-size and auto-suspend warehouses, use caching, and monitor credit usage.
Strong answer: Costs are driven by warehouse compute time and storage. Control them with appropriately sized, auto-suspending warehouses, workload isolation, leveraging result/metadata caching, avoiding unnecessary large scans, setting resource monitors, and reviewing credit usage. Storage is cheap; idle/oversized compute is the usual culprit.
Likely follow-up: What does a resource monitor do?
What the interviewer is checking: FinOps awareness.
Common mistake: Ignoring idle warehouse credits.