Snowflake Interview Questions and Answers

Snowflake is an analytic data warehouse implemented as a SaaS service. It is built on a new SQL database engine with a unique architecture built for the cloud. This cloud-based data warehouse solution was initially available on AWS as software for loading and analyzing massive volumes of data.

The most remarkable feature of Snowflake is its ability to spin up any number of virtual warehouses, allowing users to run unlimited independent workloads against the same data without risk of contention.

Three main layers make up the Snowflake architecture:

• Storage Layer: It stores data in compressed, columnar, encrypted micro-partitions. Snowflake deals with partitioning, compression, and metadata on their own
• Compute Layer (Virtual Warehouses): Where the queries live. Every warehouse is completely isolated. Your ETL won’t bog down another department’s dashboards.
• Cloud Services Layer: Handles authentication, query optimizer, transactions, access controls, and metadata. This is the brain behind the operations.

Some of the unique features of the Snowflake are listed below:

• Data Sharing
• Data Management (Zero-copy cloning and Time Travel)
• Supports Geospatial Data
• Result Caching and Dynamic Tables
• Search Optimization Service
• Security and Governance (Multi-cloud Support and HIPAA, GDPR)
• Cortex AI and Snowpark
• Iceberg Support and Streamlit.

The services layer acts as the brain of the Snowflake. In Snowflake, the Services layer authenticates user sessions, applies security functions, provides management, performs optimization, and organizes transactions.

No, Snowflake is not an ETL tool. The industry shifted to ELT, wherein raw data is sent directly to Snowflake and later transformed in the warehouse.

Dynamic Tables now natively handle declarative transformation. You define the target state, and Snowflake manages incremental refresh.

CREATE DYNAMIC TABLE daily_metrics
  TARGET_LAG = '1 hour'
  WAREHOUSE = transform_wh
AS
  SELECT DATE_TRUNC('day', event_time) AS day,
    COUNT(*) AS events, COUNT(DISTINCT user_id) AS users
  FROM raw_events GROUP BY 1;

• Dbt has taken over the transformation layer.
• Fivetran is for any fully-managed ingestion (thousands of connectors)
• Airbyte is an increasingly common alternative for ingestion as well.

For more “traditional” ELT or enterprises looking for the last-mile integration:

• Azure Data Factory for the Microsoft-centric organizations
• AWS Glue for AWS-native organizations
• Informatica Intelligent Data Management Cloud for enterprise integration
• Snowflake OpenFlow for their NiFi-GA (native ETL/CDC) offering that dropped in 2025
• Matillion, if you're using their visual ELT capabilities.

Snowflake is a cloud-native relational database, and it is specifically designed as a data warehouse for analytics. It is built entirely on an SQL database. It’s a columnar-stored relational database that works well with Power BI, Excel, Tableau, and many other tools.

Asked by: Wipro

Snowflake stores data in multiple micropartitions, which are internally optimized and compressed. The data is stored in a columnar format in Snowflake's cloud storage. 

The data objects stored by Snowflake cannot be accessed or made visible to the users. By running SQL query operations on Snowflake, you can access them.

In Snowflake, a virtual warehouse, often known as a "warehouse," is a collection of computational resources. A virtual warehouse provides the resources required for the users, such as CPU, memory, and temporary storage, to perform multiple Snowflake operations:

• Execute the SQL SELECT statements that necessitate the use of computing resources  (e.g. retrieving rows from tables and views).
• DML operations include:
• Updating table rows (DELETE , INSERT , UPDATE).
• Data Loading into tables (COPY INTO <table>).
• Data unloading from tables (COPY INTO <location>).

Whenever we load data into Snowflake, it organizes it into a compressed, columnar, and optimized format. Snowflake deals with storing data, including compression, organization, statistics, file size, and other properties associated with data storage.

All the data objects we store in Snowflake are inaccessible and invisible. We can access the data objects by executing an SQL query in Snowflake.

Snowflake is a secure, fully managed AI service built into the Snowflake AI Data Cloud that delivers generative AI and ML directly to your SQL. No Python, no external infrastructure, no data movement.

• COMPLETE (LLM text generation - Anthropic, Meta, Mistral, Google models)
• SUMMARIZE (Automatic text summarization)
• CLASSIFY (Text classification)
• SENTIMENT (Sentiment analysis)
• AI_EXTRACT (GA Oct 2025 - Extract structured data from documents)
• AI_TRANSCRIBE (Audio to text; cut pricing 60% Jun 2026)
• Cortex Analyst (Natural language to SQL)
• Cortex Search (RAG retrieval)
• Cortex AISQL (Multimodal analysis via SQL)

Asked by: Cognizant, Infosys

Snowpark is a DataFrame API for Python, Java, or Scala that runs inside a Snowflake compute. Data never leaves the platform. Everything operates under the same RBAC as SQL.

from snowflake.snowpark import Session
from snowflake.snowpark.functions import col, sum as sum_

session = Session.builder.configs(params).create()
orders = session.table("raw_orders")

report = (orders.filter(col("status") == "completed")
  .group_by("region", "category")
  .agg(sum_("amount").alias("revenue"))
  .sort(col("revenue").desc()))

report.write.mode("overwrite").save_as_table("revenue_report")

We can access through:

• Snowsight is the primary web UI. The old "Classic UI" is fully deprecated.
• Snowflake CLI (snow) To assist with operations via command line, the Snowball CLI 'snow' has replaced the old SnowSQL.
• Multiple Snowflake Connectors are supported by languages like Python, Java (JDBC) & C#. 
• A complete Snowflake Notebook for SQL & Python-based queries.
• Snowpark API for DataFrame access in Python, Java, and Scala.
• REST API for programmatic account management.

# Modern CLI (recommended)
snow sql -q "SELECT CURRENT_WAREHOUSE();"

# Legacy SnowSQL (works but no new features)
snowsql -a myaccount -u myuser -q "SELECT CURRENT_WAREHOUSE();"

Asked by: TCS, Wipro, Cognizant, Infosys

I would secure data by following these steps:

1. Mandatory MFA for all human users (SSO with Okta, Azure AD, etc. included), password only is on the deprecation path.
2. Key Pair Authentication for machine identity/service account/pipelines.
3. NetworkPolicies for restricting IP address ranges and VPN.
4. Private link for Private connections from VNET to the service.
5. Tri Secret Secure to enable customer-managed encryption keys.
6. Dynamic Data masking, based on the roles column, will be applied.
7. RowAccess policies, row security based on user roles.
8. Column Level Security, very granular control of which user can view which columns.
9. Trust Center (2025 GA), based on posture scoring.

CREATE MASKING POLICY mask_ssn AS (val STRING)
  RETURNS STRING ->
  CASE WHEN CURRENT_ROLE() IN ('HR_ADMIN') THEN val
    ELSE 'XXX-XX-' || RIGHT(val, 4) END;

ALTER TABLE employees MODIFY COLUMN ssn
  SET MASKING POLICY mask_ssn;

Asked by: Deloitte, IBM, Infosys