Free Practice Questions for Snowflake ARA-C01 Certification

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

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)

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

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)

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

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

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

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

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

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

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

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

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