Snowflake SnowPro Advanced Data Analyst Quick Facts (2025)

Exam Domain Breakdown

Domain 1: Data Ingestion and Data Preparation (17% of the exam)

Data Ingestion and Collection

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

Section summary: This section ensures you understand how to effectively collect and ingest data into Snowflake. You will learn to identify relevant data sources, determine appropriate collection frequency, and evaluate the expected data volume, all while setting up systems that reliably bring in data for further use. By mastering these practices, you create strong foundations for all subsequent transformation and analysis steps.

The focus here is on building practical expertise for managing variety in data ingestion workflows. Whether working with streaming, batch, structured, or semi-structured inputs, this prepares you to recognize the right collection patterns and ensure that data lands in the right format for accurate and efficient analysis downstream.

Data Discovery and Transformation Needs

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

Section summary: This part deepens your ability to assess existing datasets with precision. By querying and exploring tables directly in Snowflake, you learn how to discover the relevant data assets that align with business needs. The emphasis is on determining what transformations are required to take raw inputs into usable analytical forms.

Your role is to connect business questions with data possibilities. Knowing how to examine tables, define subsets, and apply meaningful transformations allows you to refine raw inputs into actionable data sets. This ensures every subsequent analysis is accurate, meaningful, and grounded in well-prepared structures.

External Data and Marketplace Enrichment

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

Section summary: This section highlights how external data can elevate your analysis when complemented with existing assets. The Snowflake Marketplace offers datasets that you can connect to your internal tables, enriching analyses with new perspectives. With data sharing capabilities, integration becomes streamlined and effective.

The skill lies in identifying which external datasets add real value, and then capitalizing on Marketplace opportunities to create tables and views that extend your organization’s insights. This allows you to combine your internal knowledge with wider industry or demographic data, driving stronger reporting and more informed decisions.

Data Integrity and Structures

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

Section summary: Ensuring data integrity is a cornerstone of advanced analysis, and this section makes those principles central. With primary keys, foreign keys, structured joins, and constraints, you learn how to design consistently reliable data systems. These approaches strengthen trust in your datasets when applied at scale.

Applying integrity-driven structures means analysts can confidently use data without question marks around accuracy. Establishing these structures allows for predictable query results, higher-quality joins, and repeatable practices that form the scaffolding for dependable analytics.

Data Processing Implementation

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

Section summary: This section emphasizes creating automated, scalable data processing pipelines within Snowflake. By designing solutions that aggregate, enrich, and monitor data quality, you ensure workflows happen consistently and efficiently. Handling failures ensures resilience in real-world scenarios.

Automation plays a critical role in modern data platforms. You not only streamline repeated analytical steps but also reduce manual intervention, leaving more time for thoughtful analysis. Using built-in monitoring strengthens processes by ensuring smooth execution and fast recovery when issues appear.

Data Loading and Preparation

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

Section summary: This part demonstrates your ability to handle the entire data loading cycle. From staging and transformations to performing insert or update operations, you learn how to place any data type into a Snowflake environment. Addressing import errors is part of ensuring your data flow remains smooth and accurate.

Competency here showcases your role as a practitioner bridging external sources and Snowflake workflows. You become skilled at not only moving data but also validating that it arrives intact, correctly formatted, and ready for downstream tasks.

Using Functions for Enhanced Data Work

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

Section summary: Functions represent your toolkit for data manipulation. Scalar and aggregate functions empower quick transformations and summaries, while window functions assist in running calculations across specified partitions. Table and system functions expand your abilities with more advanced operations.

By mastering functions, you gain the flexibility to make your analysis more precise, contextual, and layered. From geospatial insights to metadata queries, these capabilities transform data from large collections of records into focused, high-value insights for stakeholders.

Domain 2: Data Transformation and Data Modeling (22% of the exam)

Preparing Different Data Types

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

Section summary: Working with diverse data formats is critical for modern analysts. This section ensures you can transform traditional flat files such as CSVs as well as semi-structured formats like JSON and Parquet into consumable Snowflake formats. The focus is on queries, parsing, and structural preparation.

These conversions unlock consistent evaluation and downstream query performance. This capability helps ensure that no matter what source systems your organization uses, you can render the data usable.

Data Cleaning Techniques

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

Section summary: Cleaning data improves its utility for downstream models and dashboards, making this section about reliability and accuracy. You will identify anomalies, resolve inconsistencies, and implement validation strategies. Snowflake clones also enable sandboxing or testing for specific cases.

This empowers analysts to curate cleaner datasets that support credible and accurate reporting. With strong cleaning practices, the risk of presenting misleading or incomplete insights is meaningfully diminished, setting your outputs apart.

Analytic Queries

• Given a dataset or scenario, work with and query the data. Aggregate and validate the data.
• Given a dataset or scenario, work with and query the data. Apply analytic functions.
• Given a dataset or scenario, work with and query the data. Perform pre-math calculations.
• Given a dataset or scenario, work with and query the data. Perform classifications.
• Given a dataset or scenario, work with and query the data. Perform casting to present data consistently.

Section summary: The Snowflake user must not only prepare data but also bring it alive through analytic queries. This section ensures you understand how to validate datasets, perform aggregate operations, and use built-in analytics capabilities. Casting and classifications support consistent presentations.

Through these techniques, you unlock insights hidden within collected datasets. Using randomization, ranking, and grouping, you can slice through detail and find meaningful signals behind the numbers.

Data Modeling for Business Intelligence

• Use data modeling to manipulate the data to meet BI requirements. Select and implement an effective data model.
• Use data modeling to manipulate the data to meet BI requirements. Identify when to use a data model and when to use a flattened dataset.
• Use data modeling to manipulate the data to meet BI requirements. Use different modeling techniques for the consumption layer such as dimensional or Data Vault.

Section summary: Building models aligns data with the needs of business audiences. Here you learn to select from dimensional modeling, data vaults, or flattened datasets depending on context and consumption requirements.

This section emphasizes strategic planning. Analysts learn when to apply normalization or simplification, making sure BI layers deliver fast, flexible, and understandable outcomes to decision-makers.

Optimizing Query Performance

• Optimize query performance. Understand the attributes of the Query Profile.
• Optimize query performance. Understand how to view and analyze the query execution plan.
• Optimize query performance. Troubleshoot query performance.
• Optimize query performance. Leverage result, metadata, and virtual warehouse caching.
• Optimize query performance. Use different types of database objects, such as materialized views.
• Optimize query performance. Leverage partition pruning.
• Optimize query performance. Use Time Travel and cloning features.
• Optimize query performance. Use built-in functions for traversing, flattening, and nesting semi-structured data.
• Optimize query performance. Use native data types.
• Optimize query performance. Enrich the data.

Section summary: Snowflake’s optimization functions are critical for high-efficiency operations. Query profiles, caching, partition pruning, and materialized views enable substantial performance gains. Knowing how to troubleshoot execution plans amplifies overall platform value.

This ensures you maximize Snowflake’s computational strengths. Your solutions will run faster, scale better, and save costs while producing insights with the speed modern businesses demand.

Domain 3: Data Analysis (32% of the exam)

Snowflake SQL Extensibility

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

Section summary: This part focuses on the creative use of Snowflake’s extensibility options. UDFs and stored procedures allow analysts to extend SQL functionality, add custom logic, and automate reusable workflows. Materialized views enhance performance by precomputing results.

Mastering these tools elevates query efficiency while introducing modularized approaches to repetitive tasks. This adaptability is especially valuable when tackling complex analysis or ensuring faster access to known metrics.

Descriptive and Ad Hoc Analysis

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

Section summary: This aspect covers examining big data sets and finding meaning through descriptive summaries. Using Snowsight dashboards, you highlight patterns and trends visually to communicate clear results. Ad hoc analysis plays a crucial role in investigating questions spontaneously.

Combining both methods equips you to handle planned and exploratory work alike, ensuring business teams receive timely dashboards while also retaining the agility to address new questions.

Diagnostic Analysis

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

Section summary: Diagnostic analysis emphasizes investigating the "why" behind numerical patterns. You’ll identify causes behind anomalies, trace datasets for correlations, and collect related or supplementary data. Across time, this deepens understanding of statistical patterns.

These insights form the backbone of decision-focused analytics. They ensure you not only provide numbers but connect them back to meaningful business drivers behind the outcomes.

Forecasting and Predictions

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

Section summary: Forecasting takes the step from describing history to predicting future events. Snowflake’s built-in functions coupled with statistical modeling allow you to run effective forecasting pipelines within your data environment.

This ensures your role expands from analysis to strategic planning. Providing forward-looking guidance helps teams prepare for what might come, turning raw information into proactive advantage.

Domain 4: Data Presentation and Data Visualization (29% of the exam)

Reports and Dashboards

• Given a use case, create reports and dashboards to meet business requirements. Evaluate and select the data for building dashboards.
• Given a use case, create reports and dashboards to meet business requirements. Understand the effects of row access policies and Dynamic Data Masking.
• Given a use case, create reports and dashboards to meet business requirements. Compare and contrast different chart types.
• Given a use case, create reports and dashboards to meet business requirements. Understand what is required to connect BI tools to Snowflake.
• Given a use case, create reports and dashboards to meet business requirements. Create charts and dashboards in Snowsight.

Section summary: This section highlights creating professional-grade dashboards with confidence. Selecting the right data, configuring visual access policies, and choosing the best charting techniques ensure effective communication. With Snowsight and BI connectivity, you set the stage for high-impact presentation.

You will develop the ability to tailor dashboards specifically to business requirements, making your visualized analysis relevant, targeted, and actionable.

Dashboard Maintenance

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

Section summary: Maintenance ensures dashboards deliver continued value. With automation and scheduling, dashboards remain fresh and up to date. This makes operational data available consistently for decision-makers.

Learning best practices for updates, subscriptions, and sharing ensures that insights remain seamlessly embedded into workflows across teams.

Data Visualization Principles

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

Section summary: This section makes visualization principles actionable. With filtering, editing, and selecting suitable display types, analysts transform data into visually intuitive insights. Recognizing correlations or highlighting trends with clarity drives deeper understanding.

You will refine how insights are communicated to non-technical business stakeholders. Effective visualizations are critical because they allow complex analysis to be understood quickly and acted upon appropriately.