Snowflake SnowPro Advanced - Data Engineer Certification Exam Syllabus

• Outline considerations for data loading
• Define data loading features and potential impact

- Ingest data of various formats through the mechanics of Snowflake.

• Required data formats
• Outline Stages

- Troubleshoot data ingestion.

• Identify causes of ingestion errors
• Determine resolutions for ingestion errors 

- Design, build and troubleshoot continuous data pipelines.

• Stages
• Tasks
• Streams
• Snowpipe (for example, Auto ingest as compared to Rest API)

- Analyze and differentiate types of data pipelines.

• Create User-Defined Functions (UDFs) and stored procedures including Snowpark
• Design and use the Snowflake SQL API

- Install, configure, and use connectors to connect to Snowflake.
- Design and build data sharing solutions.

• Implement a data share
• Create a secure view
• Implement row level filtering

- Outline when to use External Tables and define how they work.

• Partitioning external tables
• Materialized views
• Partitioned data unloading

• Identify underperforming queries
• Outline telemetry around the operation
• Increase efficiency
• Identify the root cause

- Given a scenario, configure a solution for the best performance.

• Scale out as compared to scale up
• Virtual warehouse properties (for example, size, multi-cluster)
• Query complexity
• Micro-partitions and the impact of clustering
• Materialized views
• Search optimization service
• Query acceleration service 

- Outline and use caching features.
- Monitor continuous data pipelines.

• Snowpipe
• Tasks
• Streams

• Time Travel
• Fail-safe

- Outline the impact of Streams on Time Travel.
- Use System Functions to analyze Micro-partitions.

• Clustering depth
• Cluster keys

- Use Time Travel and Cloning to create new development environments.

• Clone objects
• Validate changes before promoting
• Rollback changes

• Authentication methods (Single Sign-On (SSO), Key pair Authentication, Username/Password, Multi-factor Authentication (MFA))
• Role Based Access Control (RBAC)
• Column Level Security and how data masking works with RBAC to secure sensitive data

- Outline the system defined roles and when they should be applied.

• The purpose of each of the system defined roles including best practices usage in each case
• The primary differences between SECURITYADMIN and USERADMIN roles
• The difference between the purpose and usage of the USERADMIN/SECURITYADMIN roles and SYSADMIN

- Manage Data Governance.

• Explain the options available to support column level security including Dynamic Data Masking and External Tokenization
• Explain the options available to support row level security using Snowflake Row Access Policies
• Use DDL required to manage Dynamic Data Masking and Row Access Policies
• Use methods and best practices for creating and applying masking policies on data
• Use methods and best practices for Object Tagging

• Snowpark UDFs (for example, Java, Python, Scala)
• Secure UDFs
• SQL UDFs
• JavaScript UDFs
• User-Defined Table Functions (UDTFs)

- Define and create External Functions.

• Secure external functions
• Integration requirements

- Design, build, and leverage Stored Procedures.

• Snowpark stored procedures (for example, Java, Python, Scala)
• SQL Scripting stored procedures
• JavaScript stored procedures
• Transaction management

- Handle and transform semi-structured data.

• Traverse and transform semi-structured data to structured data
• Transform structured data to semi-structured data
• Understand how to work with unstructured data

- Use Snowpark for data transformation.

• Understand Snowpark architecture
• Query and filter data using the Snowpark library
• Perform data transformations using Snowpark (for example, aggregations)
• Manipulate Snowpark DataFrames