Free Practice Questions for Snowflake ARA-C01 Certification
Study with 385 exam-style practice questions designed to help you prepare for the Snowflake SnowPro Advanced: Architect (ARA-C01). All questions are aligned with the latest exam guide and include detailed explanations to help you master the material.
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Key information about Snowflake SnowPro Advanced: Architect (ARA-C01)
- Multiple choice
associate (intermediate)
Snowflake Continuing Education (CE) program (eligible ILT courses, equivalent/higher-level SnowPro Certification)
Active SnowPro Core Certified credential
2+ years of practical experience with Snowflake as an Architect in a production environment. Solution Architects, Database Architects, System Architects.
10 – 13 hours
2 years
Exam Topics & Skills Assessed
Skills measured (from the official study guide)
Domain 1: Account and Security
Subdomain 1.1: Design a Snowflake account and database strategy, based on business requirements.
- Create and configure Snowflake parameters based on a central account and any additional accounts. - 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
Subdomain 1.2: Design an architecture that meets data security, privacy, compliance, and governance requirements.
- Configure Role-Based Access Control (RBAC) hierarchy - 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 - External tokenization - Dynamic Data Masking - Row-level security - 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)
Subdomain 1.3: Outline Snowflake security principles and identify use cases where they should be applied.
- Encryption
- Network security - 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 integration
Domain 2: Snowflake Architecture
Subdomain 2.1: Outline the benefits and limitations of various data models in a Snowflake environment.
- Data models - Data vault - Star schema
- Use of key/column constraints (ENABLE/RELY/VALIDATE)
Subdomain 2.2: Design data sharing solutions, based on different use cases.
- Use cases - 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
Subdomain 2.3: Create architecture solutions that support development lifecycles as well as workload requirements.
- Data lake and environments - 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
Subdomain 2.4: Given a scenario, outline how objects exist within the Snowflake object hierarchy and how the hierarchy impacts an architecture.
- Roles
- Virtual warehouses
- Object hierarchy - Databases - Schemas - Tables - Views - Stages - File formats - Functions - Procedures - Streams and tasks
Subdomain 2.5: Determine the appropriate data recovery solution in Snowflake and how data can be restored.
- Backup/recovery - Time Travel - Table types - Costs - Availability - Query performance impacts - Data corruption impacts - Zero-copy cloning - Fail-safe
- Disaster recovery - Replication and failover
Domain 3: Data Engineering
Subdomain 3.1: Determine the appropriate data loading or data unloading solution to meet business needs.
- Data sources - 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
Subdomain 3.2: Outline key tools in Snowflake’s ecosystem and how they interact with Snowflake.
- Connectors - 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
Subdomain 3.3: Determine the appropriate data transformation solution to meet business needs.
- 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 - Flatten
- Data processing
- Stored procedures
- Streams and tasks
- Functions - External functions - Performance impacts - User-Defined Functions (UDFs) - User-Defined Table Functions (UDTFs) - Secure functions
Domain 4: Performance Optimization
Subdomain 4.1: Outline performance tools, best practices, and appropriate scenarios where they should be applied.
- Query profiling - 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
Subdomain 4.2: Troubleshoot performance issues with existing architectures.
- Use of system clustering information
- Warehouse monitoring
- Optimization techniques
- Micro-partition pruning
- Monitoring and alerting - ACCOUNT_USAGE and INFORMATION_SCHEMA views - Resource monitoring - Alerts and notifications (for example, errors, email) - Event tables (for example, logging, tracing
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