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SNOWFLAKE

Snowflake SnowPro Advanced:Data Analyst

SNOWPRO-ADVANCED-DATA-ANALYSTSnowPro Advanced Data Analyst

The SnowPro Advanced: Data Analyst certification validates your ability to apply comprehensive data analysis principles using Snowflake and its components. Designed for data analysts with hands-on production experience, it covers advanced SQL, data transformations, and predictive analysis. Earning it demonstrates you can turn raw data into business-ready insights on the Snowflake AI Data Cloud.

Practice Free Questions

388 practice questions · Updated 2025-01-01

4Domains
12Objectives
134Concepts
388Questions

SNOWPRO-ADVANCED-DATA-ANALYST Curriculum

Every domain, objective, and concept the SNOWPRO-ADVANCED-DATA-ANALYST exam measures.

Perform data analysis with SQL

1 concepts · 3 questions
  1. SQL for data analysis

Apply analytic and window functions

8 concepts · 27 questions
  1. Understanding analytic functions
  2. Syntax of analytic functions
  3. Ranking functions
  4. Window frame specification
  5. Aggregate functions as window functions
  6. Navigation functions
  7. Statistical and distribution functions
  8. Combining analytic functions with other clauses

Derive insights from datasets

10 concepts · 33 questions
  1. Data Profiling
  2. Statistical Summaries
  3. Data Aggregation
  4. Trend Analysis
  5. Correlation and Relationships
  6. Segmentation and Cohort Analysis
  7. Anomaly Detection
  8. Hypothesis Testing
  9. Data Visualization for Insights
  10. Actionable Recommendations

Present and visualize data

6 concepts · 25 questions
  1. Data Presentation Principles
  2. Visualization Types
  3. Dashboard Design
  4. Visualization Tools in Snowflake
  5. Storytelling with Data
  6. Accessibility and Inclusivity in Visualization

Build dashboards and reports with Snowsight

8 concepts · 22 questions
  1. Creating dashboards in Snowsight
  2. Adding and arranging dashboard tiles
  3. Configuring dashboard filters
  4. Managing dashboard versions and history
  5. Sharing and collaborating on dashboards
  6. Creating reports in Snowsight
  7. Scheduling and delivering reports
  8. Exporting dashboard and report data

Communicate analytical results

6 concepts · 18 questions
  1. Identify stakeholders
  2. Select appropriate visualization types
  3. Apply data storytelling techniques
  4. Use annotations and highlights
  5. Present data with clarity and accuracy
  6. Communicate insights effectively

Transform data for analysis

11 concepts · 27 questions
  1. Data transformation overview
  2. SQL functions for transformation
  3. CASE and conditional logic
  4. Joins and set operations
  5. Aggregation and grouping
  6. Pivoting and unpivoting data
  7. Handling semi-structured data
  8. Data quality and cleansing
  9. Creating and using views
  10. Using CTEs and subqueries
  11. Performance considerations in transformations

Design and use data models

6 concepts · 24 questions
  1. Data model types
  2. Dimensional modeling concepts
  3. Star and snowflake schemas
  4. Slowly changing dimensions
  5. Data model design considerations
  6. Data model implementation in Snowflake

Work with views and semi-structured data

47 concepts · 116 questions
  1. Create and manage views
  2. Use secure views
  3. Use materialized views
  4. Work with recursive views
  5. Parse semi-structured data
  6. Query semi-structured data
  7. Use the FLATTEN function
  8. Use the LATERAL flatten technique
  9. Handle nested data structures
  10. Convert between structured and semi-structured data
  11. Use the GET_PATH and similar functions
  12. Use the ARRAY and OBJECT functions
  13. Use the PARSE_JSON and TO_JSON functions
  14. Use the AS_OF and AT timestamp functions
  15. Use the SAMPLE clause
  16. Use the RESULT_SCAN function
  17. Use the TABLE function
  18. Use the GET_PATH function with arrays
  19. Use the FLATTEN function with arrays
  20. Use the FLATTEN function with objects
  21. Use the FLATTEN function with nested structures
  22. Use the FLATTEN function with multiple paths
  23. Use the FLATTEN function with lateral joins
  24. Use the FLATTEN function with the OUTER parameter
  25. Use the FLATTEN function with the MODE parameter
  26. Use the FLATTEN function with the INPUT parameter
  27. Use the FLATTEN function with the PATH parameter
  28. Use the FLATTEN function with the SEQ parameter
  29. Use the FLATTEN function with the KEY parameter
  30. Use the FLATTEN function with the VALUE parameter
  31. Use the FLATTEN function with the THIS parameter
  32. Use the FLATTEN function with the RECURSIVE parameter
  33. Use the FLATTEN function with the OUTER and RECURSIVE parameters
  34. Use the FLATTEN function with the MODE and RECURSIVE parameters
  35. Use the FLATTEN function with the INPUT and PATH parameters
  36. Use the FLATTEN function with the SEQ and KEY parameters
  37. Use the FLATTEN function with the VALUE and THIS parameters
  38. Use the FLATTEN function with the OUTER and MODE parameters
  39. Use the FLATTEN function with the RECURSIVE and OUTER parameters
  40. Use the FLATTEN function with the RECURSIVE and MODE parameters
  41. Use the FLATTEN function with the RECURSIVE, OUTER, and MODE parameters
  42. Use the FLATTEN function with the RECURSIVE, OUTER, MODE, and INPUT parameters
  43. Use the FLATTEN function with the RECURSIVE, OUTER, MODE, INPUT, and PATH parameters
  44. Use the FLATTEN function with the RECURSIVE, OUTER, MODE, INPUT, PATH, and SEQ parameters
  45. Use the FLATTEN function with the RECURSIVE, OUTER, MODE, INPUT, PATH, SEQ, and KEY parameters
  46. Use the FLATTEN function with the RECURSIVE, OUTER, MODE, INPUT, PATH, SEQ, KEY, and VALUE parameters
  47. Use the FLATTEN function with the RECURSIVE, OUTER, MODE, INPUT, PATH, SEQ, KEY, VALUE, and THIS parameters

Ingest data for analysis

9 concepts · 30 questions
  1. Data Ingestion Methods
  2. Bulk Loading with COPY INTO
  3. Continuous Data Loading with Snowpipe
  4. External Data Integration
  5. Data File Formats
  6. Staging Data
  7. Data Transformation During Ingestion
  8. Error Handling and Validation
  9. Data Ingestion Best Practices

Prepare and clean datasets

15 concepts · 43 questions
  1. Data Profiling
  2. Handling Missing Values
  3. Deduplication
  4. Data Type Conversion
  5. Standardization and Normalization
  6. Outlier Detection and Treatment
  7. String Cleaning and Parsing
  8. Data Validation and Quality Checks
  9. Feature Engineering
  10. Handling Categorical Data
  11. Data Reshaping and Pivoting
  12. Merging and Joining Datasets
  13. Handling Date and Time Data
  14. Dealing with Inconsistent or Corrupt Data
  15. Sampling and Partitioning

Configure stages and file formats

7 concepts · 20 questions
  1. Stage Types and Use Cases
  2. Creating and Managing Stages
  3. Stage Permissions and Access Control
  4. File Format Types
  5. Creating and Configuring File Formats
  6. Using Stages and File Formats in Data Loading
  7. Stage and File Format Best Practices
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Practice Free Questions

Percentages reflect share of the current practice bank, not official exam weightings — no structured per-skill weight is published for SNOWPRO-ADVANCED-DATA-ANALYST, so none is invented.