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COF-C02
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SnowPro Core Certification

The SnowPro Core Certification training teaches Snowflake architecture, security, performance, data loading/unloading, and transformation, enabling engineers and analysts to build reliable, efficient data solutions on the platform.

115
Minutes
100
Questions
750/1000
Passing Score
$175
Exam Cost
2
Languages

Who Should Take This

Data engineers, analysts, and BI developers who have 2–3 months of hands‑on Snowflake experience and aim to validate their foundational knowledge should take this exam. It prepares them to design secure, performant pipelines and manage data lifecycle confidently within enterprise environments.

What's Covered

1 Cloud data platform overview, architecture layers, storage concepts, virtual warehouses, cloud services layer, and Snowflake editions.
2 User management, role hierarchy, RBAC, DAC, network policies, MFA, SSO, key pair authentication, and column/row-level security.
3 Virtual warehouse sizing, scaling policies, resource monitors, query profiling, caching layers, clustering keys, and search optimization.
4 Stages, file formats, COPY INTO, Snowpipe, bulk vs continuous loading, semi-structured data loading, and data unloading.
5 SQL DML/DDL, semi-structured data functions, stored procedures, UDFs/UDTFs, tasks, streams, Snowpark, and transactions.
6 Time Travel, Fail-safe, cloning, replication, failover, secure data sharing, data exchanges, and reader accounts.

Exam Structure

Question Types

  • Multiple Choice
  • Multiple Select

Scoring Method

Scaled scoring from 0 to 1000, minimum passing score of 750 (~65% correct)

Delivery Method

Online proctored via Kryterion Webassessor

Recertification

Valid for 2 years. Recertify by passing the current exam version.

What's Included in AccelaStudy® AI

Adaptive Knowledge Graph
Practice Questions
Lesson Modules
Console Simulator Labs
Exam Tips & Strategy
20 Activity Formats

Course Outline

65 learning goals
1 Snowflake Architecture & Key Concepts
3 topics

Architecture Layers

  • Describe Snowflake's three-layer architecture including the storage layer, compute layer (virtual warehouses), and cloud services layer and explain their independence
  • Describe the cloud services layer components including authentication, metadata management, query optimization, access control, and infrastructure management
  • Describe how Snowflake stores data in micro-partitions and explain how columnar storage, compression, and partition pruning optimize query performance
  • Analyze the benefits of Snowflake's separation of storage and compute and evaluate how independent scaling reduces cost compared to shared-disk and shared-nothing architectures
  • Describe Snowflake's support for multiple cloud providers (AWS, Azure, GCP) and explain how cross-cloud features enable multi-cloud data strategies

Virtual Warehouses

  • Describe virtual warehouse sizes from X-Small through 6X-Large and explain how each size doubles the compute resources and credits consumed per hour
  • Implement virtual warehouse configuration including auto-suspend, auto-resume, and statement timeout settings to balance cost and performance
  • Describe multi-cluster warehouse scaling policies including standard and economy modes and explain how they handle concurrent query queuing
  • Implement resource monitors to track and control credit consumption with configurable thresholds, notifications, and suspend actions
  • Analyze warehouse utilization metrics using the WAREHOUSE_METERING_HISTORY view to identify idle warehouses and recommend cost optimization actions

Editions and Objects

  • Describe Snowflake editions including Standard, Enterprise, Business Critical, and Virtual Private Snowflake and explain the key features available in each tier
  • Describe Snowflake database objects including databases, schemas, tables, views, stages, file formats, sequences, and streams and their hierarchical organization
  • Describe table types including permanent, transient, temporary, and external tables and explain their differences in Time Travel, Fail-safe, and storage behavior
  • Analyze Snowflake edition requirements for a given workload considering Time Travel retention, data encryption, multi-cluster warehouses, and compliance features
2 Account Access and Security
2 topics

Roles and Privileges

  • Describe the Snowflake role hierarchy including ACCOUNTADMIN, SECURITYADMIN, SYSADMIN, USERADMIN, and PUBLIC and explain their default privilege relationships
  • Implement role-based access control by creating custom roles, granting privileges on database objects, and assigning roles to users following least-privilege principles
  • Implement discretionary access control using GRANT and REVOKE with the WITH GRANT OPTION to delegate privilege management to object owners
  • Analyze a privilege configuration to identify security gaps including excessive ACCOUNTADMIN usage, missing role grants, and orphaned privileges on dropped objects
  • Implement managed access schemas to centralize privilege management under schema owners rather than individual object owners for simplified governance

Authentication and Network Security

  • Implement multi-factor authentication, SSO via SAML, and key pair authentication for different user types including human operators and service accounts
  • Implement network policies to restrict Snowflake access to allowed IP address ranges and explain how policies apply at account and user levels
  • Describe column-level security using dynamic data masking policies and row access policies and explain how they protect sensitive data without modifying underlying storage
  • Analyze authentication and network security configurations to recommend appropriate security controls for a compliance-sensitive data environment
3 Performance Concepts
2 topics

Caching and Query Optimization

  • Describe the three caching layers including result cache (24-hour), local disk cache (warehouse SSD), and remote disk cache and explain how each accelerates queries
  • Implement clustering keys on large tables to optimize micro-partition pruning and explain how natural ordering versus defined clustering affect scan efficiency
  • Implement the search optimization service to accelerate selective point lookups and explain when it provides benefit versus clustering keys
  • Analyze query profile output to identify performance bottlenecks including partition scanning, spillage to disk, network overhead, and suboptimal join strategies
  • Describe materialized views and explain how they automatically maintain precomputed query results for improved read performance on complex aggregations

Warehouse Sizing and Scaling

  • Implement warehouse sizing strategies by evaluating query complexity, data volume, and concurrency requirements to select appropriate warehouse size
  • Analyze the trade-offs between scaling up (larger warehouse) and scaling out (multi-cluster) for different workload patterns including ETL versus concurrent BI queries
4 Data Loading and Unloading
2 topics

Stages and File Formats

  • Describe internal stages (user, table, named) and external stages (S3, Azure Blob, GCS) and explain how they serve as intermediate locations for data loading
  • Implement file format objects for CSV, JSON, Avro, Parquet, and ORC to define parsing options including delimiters, compression, and error handling behavior
  • Implement the PUT and GET commands to upload files to internal stages and download files from stages to local storage
  • Implement storage integration objects to connect Snowflake external stages with cloud storage using IAM roles and managed identities instead of direct credentials

COPY INTO and Snowpipe

  • Implement COPY INTO table commands with file format options, pattern matching, and error handling settings including ON_ERROR and VALIDATION_MODE
  • Implement Snowpipe for continuous, event-driven data loading with auto-ingest configuration using cloud storage notifications
  • Implement data transformation during load using COPY INTO with column mappings, expressions, and semi-structured data extraction for ETL-on-load patterns
  • Implement COPY INTO location commands to unload data from tables to stages in specified file formats for data export and sharing
  • Analyze bulk loading versus Snowpipe performance characteristics to recommend the appropriate loading method based on data volume, frequency, and latency requirements
5 Data Transformation
3 topics

SQL and Semi-Structured Data

  • Implement DML operations including INSERT, UPDATE, DELETE, MERGE, and TRUNCATE on Snowflake tables with appropriate transaction handling
  • Implement semi-structured data querying using dot notation, bracket notation, LATERAL FLATTEN, and the VARIANT, OBJECT, and ARRAY data types
  • Implement common table expressions, window functions, and QUALIFY clause for complex analytical transformations in Snowflake SQL
  • Describe Snowflake-specific SQL features including SAMPLE, CONNECT BY, MATCH_RECOGNIZE, and PIVOT/UNPIVOT for advanced data manipulation
  • Implement sequences for generating unique numeric identifiers and explain how they differ from AUTOINCREMENT and IDENTITY column properties

Programmability

  • Implement user-defined functions (UDFs) in SQL and JavaScript to encapsulate reusable transformation logic callable from SQL statements
  • Implement stored procedures in JavaScript and Snowflake Scripting (SQL) for multi-statement procedural logic with error handling and dynamic SQL
  • Implement user-defined table functions (UDTFs) to return tabular results from custom logic for use in FROM clauses with LATERAL joins
  • Describe Snowpark and explain how it enables data transformation using Python, Java, and Scala DataFrames that execute within Snowflake's compute layer
  • Analyze the trade-offs between SQL UDFs, JavaScript UDFs, and stored procedures to recommend the appropriate programmability feature for a given transformation requirement

Tasks and Streams

  • Implement tasks to schedule SQL statements and stored procedure executions on recurring intervals or as part of dependent task trees (DAGs)
  • Implement streams to capture change data on tables including inserts, updates, and deletes for incremental ELT processing patterns
  • Implement task and stream combinations to build automated incremental data pipelines that process only changed records on each execution cycle
  • Analyze stream offset management and task dependency chains to troubleshoot stalled or missed incremental processing in automated data pipelines
6 Data Protection and Data Sharing
2 topics

Data Protection

  • Describe Time Travel and explain how it enables querying historical data, undropping objects, and cloning from past points in time within the configured retention period
  • Implement Time Travel queries using AT and BEFORE clauses with timestamps, offsets, and statement IDs to access historical table states
  • Describe Fail-safe and explain how it provides a 7-day recovery window beyond Time Travel for catastrophic data recovery by Snowflake support
  • Implement zero-copy cloning to create instant, storage-efficient copies of databases, schemas, and tables for development, testing, and backup purposes
  • Implement database replication and failover across Snowflake accounts and cloud regions for disaster recovery and business continuity
  • Analyze data protection strategies to recommend appropriate Time Travel retention, cloning schedules, and replication configurations based on RPO and RTO requirements
  • Implement UNDROP commands to recover accidentally dropped tables, schemas, and databases using Time Travel metadata before the retention period expires

Secure Data Sharing

  • Describe Snowflake Secure Data Sharing and explain how it enables real-time, zero-copy data access between Snowflake accounts without data movement
  • Implement outbound shares using CREATE SHARE with grants on databases, schemas, and objects to share data with specified consumer accounts
  • Implement reader accounts to provide data access to consumers who do not have their own Snowflake account with provider-managed compute resources
  • Describe the Snowflake Marketplace and explain how data providers list and monetize data products for discovery and consumption by other organizations
  • Analyze data sharing security considerations including secure views, secure UDFs, and share restrictions to ensure shared data does not expose sensitive underlying information

Certification Benefits

Salary Impact

$130,000
Average Salary

Related Job Roles

Data Engineer Data Analyst Database Administrator BI Developer Cloud Data Architect Analytics Engineer

Industry Recognition

The SnowPro Core certification is the foundational Snowflake credential, validating core platform knowledge for the fastest-growing cloud data platform with over 9,000 customers globally.

Scope

Included Topics

  • SnowPro Core Certification exam domains: Snowflake cloud data platform architecture including virtual warehouses, storage, and cloud services layer (25%); account access and security including roles, privileges, RBAC, network policies, and MFA (20%); performance concepts including virtual warehouse sizing, caching, clustering, and query optimization (10%); data loading and unloading including COPY INTO, Snowpipe, stages, file formats, and data transformation during load (10%); data transformation including SQL functions, stored procedures, UDFs, tasks, streams, and Snowpark (20%); data protection and data sharing including Time Travel, Fail-safe, cloning, replication, and secure data sharing (15%).

Not Covered

  • Snowflake Advanced Architect certification content
  • Third-party ETL/ELT tool configuration (Fivetran, dbt Cloud, Matillion)
  • Machine learning and ML model deployment on Snowflake
  • Snowflake Native Apps Framework development
  • Apache Iceberg Tables implementation details
  • Cost attribution and FinOps optimization strategies
  • Snowflake Cortex AI features

Official Exam Page

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