SnowPro Advanced Architect対策メモ
SNOWPRO ADVANCED: ARCHITECT EXAM STUDY GUIDE（2022/9/7版）に記載のある内容を参考にしつつ、参考Docを試験ガイドの項目にマッピングしたもの。SNOWPRO ADVANCED: ARCHITECT EXAM STUDY GUIDEが頻繁にアップデートされていることとリンク切れをたまに起こしているところは注意する必要があるかも。また試験の使用言語が英語しかないので、単語に慣れる意味でも英語版のマニュアルや資料を読もうと思う。
公式ガイド
https://www.snowflake.com/certifications/?lang=ja
試験ガイド
1.0 Domain: Account and Security
1.1 Design a Snowflake account and database strategy, based on business requirements.
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Create and configure Snowflake parameters based on a central account and any additional accounts.
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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.
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Configure Role Based Access Control (RBAC) hierarchy
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System roles and associated best practices
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Data Access
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Data Security
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Compliance
1.3 Outline Snowflake security principles and identify use cases where they should be applied.
Managing Security in Snowflake — Snowflake Documentation
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Encryption
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Network security
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User, Role, Grants provisioning
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Authentication
STUDY GUIDEには参考資料として掲載されているが、上記の1.1~1.3のどこに紐づくかわからないもの
WhitePaper/Read Assets
- Cloud Data Platform Security: How Snowflake Sets the Standard
Lab Guides
- Snowflake Pattern - Security - Access to Sensitive Objects
- Snowflake Pattern - Security - Authentication
- Snowflake Pattern - Security - Network Architecture
2.0 Domain: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.
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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
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Data Exchange
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Data Sharing Methods
2.3 Create architecture solutions that support Development Lifecycles as well as workload requirements.
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Data Lake and Environments
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Workloads
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Development lifecycle support
2.4 Given a scenario, outline how objects exist within the Snowflake Object hierarchy and how the hierarchy impacts an architecture.
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Roles
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Warehouses
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Object hierarchy
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Database
2.5 Determine the appropriate data recovery solution in Snowflake and how data can be restored.
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Backup/Recovery
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Disaster Recovery
Lab Guides
- Building an application on Snowflake with data from Snowflake Data Marketplace
- Getting Started with Time Travel
- Data Modeling | Snowflake
WhitePaper/Read Assets
- SNO-eBook-7-Reference-Architectures-for-Application-Builders-AppHealth-SecurityAna.pdf
- Support Multiple Data Modeling Approaches with Snowflake
- Data Modeling in the Age of JSON and Schema-on-Read - Snowflake Blog
- Different Types of Objects in Snowflake
3.0 Domain: 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.
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Connectors
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Connectors & Drivers — Snowflake Documentation
- Kafka
- Spark
- Python
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Drivers
- JDBC
- OBDC
- API endpoints
- SnowSQL
3.3 Determine the appropriate data transformation solution to meet business needs.
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Materialized Views, Views and Secure Views
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Staging layers and tables
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Querying semi-structured data
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Data processing
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Stored Procedures
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Streams and Tasks
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Functions
WhitePaper/Read Assets
- Snowflake For Data Engineering - Easily Ingest, Transform, And Deliver Data For Up-To-The-Moment Ins
- Using Streams and Tasks in Snowflake
- Masking Semi-Structured Data with Snowflake - Snowflake Blog
- Easy Continuous Data Pipelines with GA of Streams and Tasks - Snowflake Blog
Lab Guides
- Building a Real-Time Data Vault in Snowflake
- Enrich Salesforce data with Snowflake to deliver your Customer 360
- Auto-Ingest Twitter Data into Snowflake
- Build a Recommendation Engine with AWS SageMaker
- Getting Started with Snowpipe
- Accelerating Data Teams with dbt Cloud & Snowflake
- Getting Started With User-Defined SQL Functions
- Getting Started with Python
- Getting Started with SnowSQL
4.0 Domain: Performance Optimization
4.1 Outline performance tools, best practices, and appropriate scenarios where they should be applied.
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Query profiling
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Virtual Warehouse configuration
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Clustering
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Search Optimization
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Caching
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Query rewrite
4.2 Troubleshoot performance issues with existing architectures.
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JOIN explosions
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Warehouse selection (scaling up as compared to scaling out)
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Best practices and optimization techniques
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Duplication of data
WhitePaper/Read Assets
Lab Guides
- Resource Optimization: Performance
- Resource Optimization: Billing Metrics
- Resource Optimization: Setup & Configuration
- Resource Optimization: Usage Monitoring
- Automating Data Pipelines to Drive Marketing Analytics with Snowflake & Fivetran
- Building a Data Application
- DevOps: Database Change Management with schemachange and GitHub
関連しているかもしれない記事
- Snowflakeでログイン履歴を確認する
- Snowflakeのアクセス制御や権限管理
- S3(外部ステージ)からSnowflakeにデータロードする
- SnowPro Core認定の試験ガイド対策
- SnowflakeのTime Travel