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 file formats
• Ingestion of structured, semi-structured, and unstructured data
• Implementation of stages and file formats

- 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)
• Snowpipe Streaming

- Analyze and differentiate types of data pipelines.

• Create User-Defined Functions (UDFs)
• Design and use the Snowflake SQL API
• Create data pipelines in Snowpark

- Install, configure, and use connectors to connect to Snowflake.

• Kafka connectors
• Spark connectors
• Python connectors

- Design and build data sharing solutions.

• Implement a data share
• Create and manage views
• Implement row-level filtering
• Share data using the Snowflake Marketplace
• Share data using a listing

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

• Manage external tables
• Manage Iceberg tables
• Perform general table management
• Manage schema evolution
• Unload data

• 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 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
• Snowpark-optimized warehouses
• Caching features

- Monitor continuous data pipelines.

• Snowflake objects
  1. Tasks
  2. Streams
  3. Snowpipe Streaming
  4. Alerts
• Notifications
• Data Quality and data metric function monitoring

• Time Travel
  1. Impact of streams
• Fail-safe
• Cross-region and cross-cloud replication

- 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

• Apply object tagging and classifications
• Use data classification to monitor data
• Manage data lineage and object dependencies
• Monitor data quality

- Establish and maintain data protection.

• Implement column-level security
  1. Use in conjunction with Dynamic Data Masking
  2. Use in conjunction with external tokenization
  3. Use projection policies
• Use data masking with Role-Based Access Control (RBAC) to secure sensitive data
• Explain the options available to support row-level security using Snowflake row access policies
  1. Use aggregation policies
• Use DDL to manage Dynamic Data Masking and row access policies
• Use best practices to create and apply data masking policies
• Use Snowflake Data Clean Rooms to share data
  1. Use the web-app
• Use the Snowflake developer API

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

- Define and create External Functions.

• Secure external functions
• Work with external functions

- Design, build, and leverage Stored Procedures.

• Snowpark stored procedures (for example, Java, Python, Scala)

- Handle and transform semi-structured data.

• SQL Scripting stored procedures
• JavaScript stored procedures
• Transaction management
• Traverse and transform semi-structured data to structured data
• Transform structured data to semi-structured data

- Handle and process unstructured data.

• Use unstructured data
  1. URL types
• Use directory tables
• Use the Rest API

- 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