SnowPro Advanced: Data Analyst Preparation Details
The SnowPro Advanced: Data Analyst (DAA-C01) exam validates advanced skills in data ingestion, transformation, analysis, and visualization using the Snowflake AI Data Cloud. This guide breaks down every domain, task, and objective from the official exam guide, pairing each with verified Snowflake documentation. You can also explore more Snowflake certification study guides on the Snowflake Certification category to keep building your skills.
SnowPro Advanced: Data Analyst Materials
| Coursera | Snowflake Data Engineering Professional Certificate |
| Udemy | Snowflake Snowpro Advanced: Data Analyst Exam |
Domain 1.0: Data Ingestion and Data Preparation
1.1 Use a collection system to retrieve data.
Retrieve data from a source (Structured (CSV), Semi-structured (e.g., Parquet, Avro, ORC, JSON, or XML), Unstructured, Synthetic Data Generation)
Introduction to Unstructured Data Support
Using synthetic data in Snowflake
1.2 Perform data discovery to identify what is needed from the available datasets.
Query tables in Snowflake to assess: (Data elements including statistics maintained by Snowflake, The elements that are required for business goals (using BI reports or SQL analysis), The level of data granularity required)
Querying data using worksheets
Querying Metadata for Staged Files
Evaluate which transformations are required: (Perform table joins and set operations (e.g., UNION, UNION ALL, INTERSECT, and MINUS), Perform data filtering and/or transformation, ASOF JOINS)
Set Operators (UNION, MINUS, INTERSECT)
Use commands to read metadata and/or to alter context (e.g., DESCRIBE, SHOW, USE)
Querying Metadata for Staged Files
1.3 Enrich data by identifying and accessing relevant data from the Snowflake Marketplace.
Find external data sets that correlate with available data
Snowflake Marketplace and Listings
Use Secure Data Sharing to enrich existing data sets (e.g., Data from Snowflake Marketplace, The Internal Marketplace, Private Listings, and Listings)
Introduction to Secure Data Sharing
Data sharing and collaboration in Snowflake
Snowflake Marketplace and Listings
Create tables and views
1.4 Use best practice considerations relating to data integrity structures.
Define primary keys for tables
Perform table joins between parent/child tables (Implement constraints)
1.5 Implement data processing solutions.
Cleanse, conform, and enrich data
Introduction to data quality checks
Automate and implement data pipelines (Scheduling)
Respond to processing failures (Use logging and monitoring solutions, Auditing, Data lineage)
1.6 Given a scenario, prepare data and load into Snowflake.
Load files using Snowsight
Loading Using the Web Interface (Limited)
Load data from external/internal stages into a table
Bulk Loading Using COPY into Table
Introduction to External Tables
Load different types of data (Tabular data/structured data, Semi-structured data, Unstructured data)
Introduction to Unstructured Data Support
Perform general DML (INSERT, UPDATE, and DELETE)
Identify and resolve data import errors
Prepare external tables
Introduction to External Tables
1.7 Given a scenario, use Snowflake functions.
Scalar functions
Aggregate functions
Summary of Aggregate Functions
Window functions
Table functions
System functions
SYSTEM$ESTIMATE_QUERY_ACCELERATION
Geospatial functions
AI functions
User-Defined Functions (UDFs)
Overview of User-Defined Functions
ML functions (Classification, Top Insights, Anomaly Detection)
Overview of ML-Powered Functions
Domain 2.0: Data Transformation and Data Modeling
2.1 Prepare different data types into a consumable format.
CSV
Transforming Data During a Load
JSON (query and parse)
Parquet
XML
2.2 Given a dataset, clean the data.
Identify and analyze data quality issues
Introduction to data quality checks
Handle erroneous and ambiguous data (Handle duplications, Handle nulls)
Convert data types
Use clones as required by specific use-cases
Understanding & Using Time Travel
Use Data Metric Functions (DMFs)
2.3 Given a dataset or scenario, work with and query the data.
Aggregate and validate the data
Summary of Aggregate Functions
Introduction to data quality checks
Apply analytic/window functions
Perform pre-math calculations (e.g., randomization, ranking, grouping, min/max)
Perform casting – change data types to ensure data can be presented consistently
Enrich the data (Use cartesian joins, sub-queries, CTEs, and union queries, Work with hierarchical data, Use sampling, approximation, and estimation features)
Estimating the Number of Distinct Values
Use Time Travel and cloning features
Understanding & Using Time Travel
Use built-in functions for traversing, flattening, transforming, and nesting semi-structured data
Use native data types
2.4 Use data modeling to manipulate the data to meet BI requirements.
Select and implement an effective data model
Working with Materialized Views
Identify when to use a data model and when to use a flattened data set
Use different modeling techniques for the consumption layer (e.g., dimensional, Data Vault)
Working with Materialized Views
2.5 Optimize query performance.
Understand how to view and analyze the query execution plan
Analyzing Queries Using Query Profile
Troubleshoot query performance (Leverage partition pruning, Leverage clustering keys)
Micro-partitions & Data Clustering
Clustering Keys & Clustered Tables
Analyzing Queries Using Query Profile
Leverage result, metadata, and virtual warehouse caching
Analyzing Queries Using Query Profile
Use search optimization service and virtual warehouse features such as the query acceleration services
Domain 3.0: Data Analysis
3.1 Use SQL extensibility features.
User-Defined Functions (UDFs)
Overview of User-Defined Functions
User-Defined Table Functions (UDTFs)
Stored procedures (Asynchronous Stored Procedure)
Calling stored procedures asynchronously
Regular, secure, and materialized views
Working with Materialized Views
3.2 Perform descriptive analyses.
Summarize large data sets using Snowsight dashboards (Create a reusable filter)
Visualizing Data With Dashboards
Perform exploratory ad-hoc analyses using Notebooks and worksheets to describe data
Working with Worksheets in Snowsight
Querying data using worksheets
3.3 Perform diagnostic analyses.
Find reasons/causes of anomalies or patterns in historical data
Collect related data
Querying data using worksheets
Identify demographics and relationships
Summary of Aggregate Functions
Analyze statistics and trends
3.4 Perform forecasting.
Use statistics and built-in functions
Make predictions based on data
Domain 4.0: Data Presentation and Data Visualization
4.1 Given a use case, create reports and dashboards to meet business requirements.
Evaluate and select the data for building dashboards (Set the contexts (e.g., database, schema, virtual warehouse, role), Create and run SQL queries, Apply naming conventions to data columns and queries, Sort and filter data)
Visualizing Data With Dashboards
Querying data using worksheets
Understand the effects of row access policies and Dynamic Data Masking
Understanding column-level security
Compare and contrast different chart types (e.g., bar charts, scatter plots, heat grids, scorecards)
Visualizing Data With Dashboards
Understand what is required to connect BI tools to Snowflake
Create charts and dashboard in Snowsight (Create and manage custom filters)
Visualizing Data With Dashboards
4.2 Given a use case, maintain reports and dashboards to meet business requirements.
Build automated and repeatable tasks
Operationalize data for consumption
Working with Materialized Views
Manage and share Snowsight dashboards
Visualizing Data With Dashboards
Working with Worksheets in Snowsight
Configure subscriptions and updates
Visualizing Data With Dashboards
4.3 Given a use case, incorporate visualizations for dashboards and reports.
Present data for business-use analyses
Visualizing Data With Dashboards
Querying data using worksheets
Identify patterns and trends
Identify correlations among variables
Summary of Aggregate Functions
Troubleshoot common issues with data analytics dashboard and reports
Analyzing Queries Using Query Profile
Visualizing Data With Dashboards
Customize data presentations using filtering and editing techniques
Wrapping Up SnowPro Advanced: Data Analyst
This SnowPro Advanced: Data Analyst study guide covered all four exam domains, from data ingestion and transformation to analysis and visualization in Snowflake. Working through each objective and its linked documentation will build the practical, hands-on knowledge the DAA-C01 exam expects. You can also explore more Snowflake certification study guides on the Snowflake Certification category to keep building your skills. Have a question or tip? Leave a comment below.
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