Snowflake SnowPro Advanced - Data Analyst Certification Exam Syllabus

Use a collection system to retrieve data.
- Assess how often data needs to be collected
- Identify the volume of data to be collected
- Identify data sources
- Retrieve data from a source

Perform data discovery to identify what is needed from the available datasets.
- Query tables in Snowflake
- Evaluate which transformations are required

Enrich data by identifying and accessing relevant data from the Snowflake Marketplace.
- Find external data sets that correlate with available data
- Use data shares to join data with existing data sets
- Create tables and views

Outline and use best practice considerations relating to data integrity structures.
- Primary keys for tables
- Perform table joins between parent/child tables
- Constraints

Implement data processing solutions.
- Aggregate and enrich data
- Automate and implement data processing
- Respond to processing failures
- Use logging and monitoring solutions

Given a scenario, prepare data and load into Snowflake.
- Load files using Snowsight
- Load data from external/internal stages into a Snowflake table
- Load different types of data
- Perform general DML (insert, update, delete)
- Identify and resolve data import errors

Given a scenario, use Snowflake functions.
- Scalar functions
- Aggregate functions
- Window functions
- Table functions
- System functions
- Geospatial functions

Prepare different data types into a consumable format.
- CSV
- JSON (query and parse)
- Parquet

Given a dataset, clean the data.
- Identify and analyze data anomalies
- Handle erroneous data
- Validate data types
- Use clones as required by specific use-cases

Given a dataset or scenario, work with and query the data.
- Aggregate and validate the data.
- Apply analytic functions
- Perform pre-math calculations (examples, randomization, ranking, grouping, min/max)
- Perform classifications
- Perform casting - change data types to ensure data can be presented consistently
- Enrich the data
- Leverage partition pruning
- Use Time Travel and cloning features
- Use built-in functions for traversing, flattening, and nesting semi-structured data
- Use native data types

Use data modeling to manipulate the data to meet BI requirements.
- Select and implement an effective data model
- Identify when to use a data model and when to use a flattened data set
- Use different modeling techniques for the consumption layer (for example, dimensional, Data Vault)

Optimize query performance.
- Understand the attributes of the Query Profile
- Understand how to view and analyze the query execution plan
- Troubleshoot query performance
- Leverage result, metadata, and virtual warehouse caching
- Use of different types of database objects, such as materialized views

Use SQL extensibility features.
- User-Defined Functions (UDFs)
- Stored procedures
- Regular, secure, and materialized views

Perform a descriptive analysis.
- Summarize large data sets using Snowsight dashboards
- Perform exploratory ad-hoc analyses

Perform a diagnostic analysis.
- Find reasons/causes of anomalies or patterns in historical data
- Collect related data
- Identify demographics and relationships
- Analyze statistics and trends

Perform forecasting.
- Use statistics and built in functions
- Make predictions based on data

Given a use case, create reports and dashboards to meet business requirements.
- Evaluate and select the data for building dashboards
- Understand the effects of row access policies and Dynamic Data Masking
- Compare and contrast different chart types (for example, bar charts, scatter plots, heat grids, scorecards)
- Understand what is required to connect BI tools to Snowflake
- Create charts and dashboard in Snowsight

Given a use case, maintain reports and dashboards to meet business requirements.
- Build automated and repeatable tasks
- Operationalize data
- Store and update data
- Manage and share Snowsight dashboards
- Configure subscriptions and updates

Given a use case, incorporate visualizations for dashboards and reports.
- Present data for business use analyses
- Identify patterns and trends
- Identify correlations among variables
- Customize data presentations using filtering and editing techniques