Free Practice Questions for Snowflake DAA-C01 Certification
- Exam guide version:
- October 13, 2025
- Guide checked for updates:
- 9 Oct 2026
- Question bank created:
- 5 Oct 2026
- Question bank last updated:
- 6 Oct 2026
Study with 360 exam-style practice questions designed to help you prepare for the Snowflake SnowPro Advanced: Data Analyst (DAA-C01). All questions are aligned with the latest exam guide and include detailed explanations to help you master the material.
Recommended
This is the recommended version, generated by more capable models with more context.
Your progress
LessonsNew
19 documentation-grounded lessons, one per exam-guide subdomain — every claim cited to the official docs.Based on the official docs as of 4 Oct 2026
Start Practicing
Start a quiz
Practice with randomly mixed questions from all topics
Exam experiences
Pass and fail outcomes from candidates who prepared here — advice, scores, and prep time.
Your saved questions
Open the list of questions you bookmarked during practice for this exam.
Study notes
Private notes per question, grouped by exam domain — opens on its own page, not inline on this overview.
Quiz History
Exam Details
Key information about Snowflake SnowPro Advanced: Data Analyst (DAA-C01)
- Multiple choice
associate (intermediate)
Completion of eligible ILT courses or earning a higher-level SnowPro Certification
Active SnowPro Core Certified credential
Snowflake Data Analysts, ELT Developers, Business Intelligence Professionals, Analytics Engineers with 1+ year of Snowflake experience
10-13 hours
2 years
Exam Topics & Skills Assessed
Skills measured (from the official study guide)
1: 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
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 ● 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 ● Use commands to read metadata and/or to alter context (e.g., DESCRIBE, SHOW, USE)
1.3: Enrich data by identifying and accessing relevant data from the Snowflake Marketplace.
● Find external data sets that correlate with available data ● Use Secure Data Sharing to enrich existing data sets (e.g., Data from Snowflake Marketplace, The Internal Marketplace, Private Listings, 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 ● 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 ● Load data from external/internal stages into a table ● Load different types of data ○ Tabular data/structured data ○ Semi-structured data ○ Unstructured data ● Perform general DML (INSERT, UPDATE, and DELETE) ● Identify and resolve data import errors ● Prepare external tables
1.7: Given a scenario, use Snowflake functions.
● Scalar functions ● Aggregate functions ● Window functions ● Table functions ● System functions ● Geospatial functions ● AI functions ● User-Defined Functions (UDFs) ● ML functions ○ Classification ○ Top Insights ○ Anomaly Detection
2: Data Transformation and Data Modeling
2.1: Prepare different data types into a consumable format.
● CSV ● JSON (query and parse) ● Parquet ● XML
2.2: Given a dataset, clean the data.
● Identify and analyze data quality issues ● Handle erroneous and ambiguous data ○ Handle duplications ○ Handle nulls ● Convert data types ● Use clones as required by specific use-cases ● Use Data Metric Functions (DMFs) ● Use Time Travel and cloning features ● Use built-in functions for traversing, flattening, transforming, and nesting semi-structured data ● Use native data types
2.3: Given a dataset or scenario, work with and query the data.
● Aggregate and validate the data ● 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
2.4: 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 (e.g., dimensional, Data Vault)
2.5: Optimize query performance.
● Understand how to view and analyze the query execution plan ● Troubleshoot query performance ○ Leverage partition pruning ○ Leverage clustering keys ● Leverage result, metadata, and virtual warehouse caching ● Use search optimization service and virtual warehouse features such as the query acceleration services
3: Data Analysis
3.1: Use SQL extensibility features.
● User-Defined Functions (UDFs) ● User-Defined Table Functions (UDTFs) ● Stored procedures ○ Asynchronous Stored Procedure ● Regular, secure, and materialized views
3.2: Perform descriptive analyses.
● Summarize large data sets using Snowsight dashboards ○ Create a reusable filter ● Perform exploratory ad-hoc analyses using Notebooks and worksheets to describe data
3.3: Perform diagnostic analyses.
● Find reasons/causes of anomalies or patterns in historical data ● Collect related data ● Identify demographics and relationships ● Analyze statistics and trends
3.4: Perform forecasting.
● Use statistics and built-in functions ● Make predictions based on data
4: 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 ● Understand the effects of row access policies and Dynamic Data Masking ● Compare and contrast different chart types (e.g., bar charts, scatter plots, heat grids, scorecards) ● Understand what is required to connect BI tools to Snowflake ● Create charts and dashboard in Snowsight ○ Create and manage custom filters
4.2: Given a use case, maintain reports and dashboards to meet business requirements.
● Build automated and repeatable tasks ● Operationalize data for consumption ● Manage and share Snowsight dashboards ● Configure subscriptions and updates
4.3: Given a use case, incorporate visualizations for dashboards and reports.
● Present data for business-use analyses ● Identify patterns and trends ● Identify correlations among variables ● Troubleshoot common issues with data analytics dashboard and reports ● Customize data presentations using filtering and editing techniques
Techniques & products