Snowflake Architect ARA-C01 Certification Exam!

The SnowPro Advanced: Architect Practice Exam Assess candidate's advanced knowledge and skills used to apply comprehensive architect solutions using Snowflake. The exam will assess skills through scenario-based questions and real-world examples.

This certification Mock test will test the ability to:

● Design an end-to-end data flow from source to consumption using the Snowflake Data Cloud.

● Design and deploy a data architecture that meets business, security, and compliance requirements.

● Select appropriate Snowflake and third-party tools to optimize architecture performance.

● Design and deploy a shared data set using the Snowflake Marketplace and Data Exchange.

Target Audience:

2+ years of practical experience with Snowflake as an Architect in a production environment. In addition, successful candidates may have:

● Hands-on expertise with SQL and SQL analytics

● Experience building out a complex ETL/ELT pipeline

● Experience implementing security and compliance requirements

● Working with different data modeling techniques.

Having coding experience outside of SQL and DevOps/DataOps design experience is a plus.

This exam is designed for:

● Solution Architects

● Level 1/Level 2 Architects

● System Architects

● Senior Consultants

Domain Domain Weightings on Exams

1.0 Accounts and Security     25%

2.0 Snowflake Architecture      30%

3.0 Data Engineering               25%

4.0 Performance Optimization 20%

SNOWPRO ADVANCED: ARCHITECT DOMAINS & OBJECTIVES

Domain 1.0: Account and Security

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.

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

1.2 Design an architecture that meets data security, privacy, compliance, and governance requirements.

● Configure Role Based Access Control (RBAC) hierarchy

● System roles and associated best practices

● Data access

● Data security

● Compliance

1.3 Outline Snowflake security principles and identify use cases where they should be applied.

● Encryption

● Network security

● User, role, grants provisioning

● Authentication

Domain 2.0: Snowflake Architecture

2.1 Outline the benefits and limitations of various data models in a Snowflake environment.

● Data models

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 platforms

● Snowflake Marketplace

● Data Exchange

● Data sharing methods

2.3 Create architecture solutions that support development lifecycles as well as workload requirements.

● Data lakes and environments

● Workloads

● Development lifecycle support

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

● Database

2.5 Determine the appropriate data recovery solution in Snowflake and how data can be restored.

● Backup/recovery

● Disaster recovery

Domain 3.0: Data Engineering

3.1 Determine the appropriate data loading or data unloading solution to meet business needs.

● Data sources

● Ingestion of the data

● Architecture changes

● Data unloading

3.2 Outline key tools in Snowflake’s ecosystem and how they interact with Snowflake.

● Connectors

○ Kafka

○ Spark

○ Python

● Drivers

○ JDBC

○ ODBC

● API endpoints

● SnowSQL

3.3 Determine the appropriate data transformation solution to meet business needs.

● Materialized views, views, and secure views

● Staging layers and tables

● Querying semi-structured data

● Data processing

● Stored procedures

● Streams and tasks

● Functions

○ External functions

○ User-Defined Functions (UDFs)

Domain 4.0: Performance Optimization

4.1 Outline performance tools, best practices, and appropriate scenarios where they should be applied.

● Query profiling

● Virtual warehouse configuration

● Clustering

● Search optimization service

● Caching

● Query rewrite

4.2 Troubleshoot performance issues with existing architectures.

● JOIN explosions

● Virtual warehouse selection (scaling up as compared to scaling out)

● Best practices and optimization techniques

● Duplication of data

● Monitoring and alerting

○ Statistics

○ Resource monitoring

○ Account usage and information schema

Happy Learning !!