Snowflake SnowPro Advanced - Architect Certification Exam Syllabus

• Parameters (all levels)
  - Account parameters
  - Object parameters
  - Session parameters
• Outline the Snowflake parameter hierarchy and the relationship between the parameter types.

- List the benefits and limitations of one Snowflake account as compared to multiple Snowflake accounts.

• Isolate or segment accounts
• Key considerations and constraints when defining an account strategy
• Features/capabilities that can be leveraged across accounts
• Identify use cases that are appropriate for account strategies

• Privilege inheritance
• Database roles
• System roles and associated best practices
• Functional roles compared to access roles
• Secondary roles

- Data Access

• Storage integrations

- Data Security

• Secure views
• Data Governance
  - Column-level security
  1. External tokenization
  2. Dynamic Data Masking
  - Row-level security
  1. Row access policies
  - Aggregate policies
  - Projection policies
  - Data lineage and dependencies
  - Object tagging
• Compliance
• Features of the different Snowflake editions
  - Payment Card Industry (PCI) Security Standard
  - Personal Identifiable Information (PII)/ Personal Health Information (PHI)

• Network policies
• Network rules
• External access
• Access control privileges
• Private connectivity
  - AWS PrivateLink
  - Azure Private Link
  - Google Cloud Private Service Connect

- User, role, and grants provisioning
- Authentication

• Authentication policies
• Federated authentication
• Single Sign-on (SSO)
• OAuth
• Multi-Factor Authentication (MFA)
• Key-pair authentication
• Security integrations

• Data vault
• Star schema

- Use of key/column constraints (ENABLE/RELY/VALIDATE)

• Sharing within the same organization/same Snowflake account
• Sharing within a cloud region
• Sharing across cloud regions
• Sharing between different Snowflake accounts
• Sharing to a non-Snowflake customer
• Sharing across cloud providers
• Sharing using Snowflake Data Clean Rooms

- Snowflake Marketplace
- Data Exchange
- Data sharing methods

• Configure shares, account parameters, and privileges
• Security patterns for data sharing
• Outline the purpose, benefits, and capabilities of the multiple data sharing methods
• Cross-Cloud Auto-Fulfillment

• Storage directory structure
• Zones (data warehouse layers)
• Support of DevOps/DataOps principles
• Production/development/sandbox
• Data workloads
• Data warehouse
• ELT/ETL

- Development lifecycle support

• Migration
• Deployment
  - CI/CD
  - Snowflake CLI
• Git integration
• Rollback process

- Outline basic AI/ML pipelines and applications

• Snowpark Container Services
• Snowflake ML functions
• Cortex LLM functions
• Streamlit
• Snowflake Native App Framework

• Databases
  - Schemas
  1. Tables
  2. Views
  3. Stages
  4. File formats
  5. Functions
  6. Procedures
  7. Streams and tasks

• Time Travel
  - Table types
  - Costs
  - Availability
  - Query performance impacts
• Data corruption impacts
• Zero-copy cloning
• Fail-safe

- Disaster recovery

• Replication and failover

Data Engineering - 20-25%

• Data at rest
• Data in motion
• External sources and formats
• Streaming data
  - Snowpipe
  - Change Data Capture (CDC)
• OLTP/RDBMS sources
• API sources

- Data ingestion

• Bulk file upload
• Snowpipe
• Snowpipe streaming
• External tables
• Reload process (load history)
• Incremental updates compared to full updates
• Iceberg tables (managed and unmanaged)
• Parameters for copying data and addressing data handling errors

- Architecture changes

• Schema detection and table schema evolution
• Data source changes

- Data unloading

• Kafka
• Spark
• Python
• Snowflake Connector for ServiceNow
• Snowflake Connector for Google Analytics

- Drivers

• JDBC
• ODBC

- API endpoints

• Use of system$allowlist
• SQL API

- SnowSQL
- Snowflake CLI
- Snowpark

• Python
• Scala
• Java

- Views and tables

• Benefits, limitations, properties
• Relationship and impact between the view and data types
• Impact of costs
• Dynamic tables

- Staging layers and tables
- Querying semi-structured data

• Flattened

- Data processing
- Stored procedures
- Streams and tasks
- Functions

• External functions
  - Performance impacts
• User-Defined Functions (UDFs)
• User-Defined Table Functions (UDTFs)
• Secure functions

Performance Optimization - 20-25%

• Interpret a Query Profile, identify bottlenecks, and outline recommendations
• Metadata functions
• Warehouse queuing
• Warehouse spilling

- Virtual warehouse configurations

• Auto-suspend/resume
• Scale up/down (resizing)
• Scale in/out (multi-cluster warehouse/auto-scaling)
• Query acceleration service
• Snowpark-optimized warehouses

- Clustering

• Natural clustering
• Auto-clustering
• Clustering keys

- Search optimization service
- Caching

• Different cache layers
• Cache expiration
• Impact of costs

• Use of the Account Usage and the Information schemas
• Resource monitoring
• Alerts and notifications (for example, errors, email)
• Event tables (for example, logging, tracing)