Snowflake SnowPro Advanced Data Scientist Quick Facts (2025)

Snowflake SnowPro Advanced Data Scientist Quick Facts

The Snowflake SnowPro Advanced Data Scientist certification empowers data professionals to demonstrate their ability to turn raw data into actionable intelligence. This exam overview provides clarity and direction so you can approach your certification journey with confidence and focus.

How does the Snowflake SnowPro Advanced Data Scientist certification build your expertise?

This certification validates your advanced data science skills within the Snowflake ecosystem, emphasizing machine learning workflows, feature engineering, model development, and deployment best practices. It is designed for data scientists who want to showcase their ability to leverage Snowflake’s native features alongside familiar tools like Python, Snowpark, and open-source frameworks. Earning this credential shows you have the practical skills to design, train, and operationalize machine learning models at scale, all while delivering meaningful business outcomes.

Exam Domains Covered (Click to expand breakdown)

Exam Domain Breakdown

Domain 1: Data Science Concepts (17% of the exam)

1.1 Define machine learning concepts for data science workloads.

• Machine Learning - Supervised learning
• Machine Learning - Unsupervised learning

1.1 summary: In this section you explore the fundamental approaches to machine learning. Supervised learning focuses on predicting outcomes from labeled datasets, while unsupervised learning emphasizes creating structure and insights from unlabeled data. The exam requires a clear understanding of how these paradigms are applied in real workloads.

You will also need to know how to map machine learning concepts to different business cases. Demonstrating these fundamentals helps you connect the theory of ML directly to actionable use in a Snowflake environment.

1.2 Outline machine learning problem types.

• Supervised Learning - Structured Data - Linear regression
• Supervised Learning - Structured Data - Binary classification
• Supervised Learning - Structured Data - Multi-class classification
• Supervised Learning - Structured Data - Time-series forecasting
• Supervised Learning - Unstructured Data - Image classification
• Supervised Learning - Unstructured Data - Segmentation
• Unsupervised Learning - Clustering
• Unsupervised Learning - Association models

1.2 summary: Here you will dive into the different ways of framing machine learning problems. From predictive modeling with regression to classification tasks in both binary and multi-class formats, the focus is on understanding when and how to select each technique.

The domain also includes unstructured data use cases such as image recognition alongside unsupervised methods like clustering and association. Knowing these distinctions prepares you to align the right ML problem type with the data and business need.

1.3 Summarize the machine learning lifecycle.

• Data collection
• Data visualization and exploration
• Feature engineering
• Training models
• Model deployment
• Model monitoring and evaluation (e.g., model explainability, precision, recall, accuracy, confusion matrix)
• Model versioning

1.3 summary: This section covers the complete cycle of machine learning development. Beginning with data collection, visualization, and initial exploration, you learn how to craft features and iterate on model training.

Equally important is the post-training process: deploying models into use, monitoring performance, and ensuring version control are all core elements. The exam expects you to show proficiency across all stages of the lifecycle to ensure production-ready outcomes.

1.4 Define statistical concepts for data science.

• Normal versus skewed distributions (e.g., mean, outliers)
• Central limit theorem
• Z and T tests
• Bootstrapping
• Confidence intervals

1.4 summary: In this section you revisit foundational statistical principles. Understanding the distribution of data, the importance of the central limit theorem, and the application of various significance tests gives you the foundation for reliable analysis.

The exam also emphasizes methods like bootstrapping and interpreting confidence intervals, which provide ways to quantify uncertainty. These concepts enable you to connect statistical rigor with practical machine learning applications.

Domain 2: Data Preparation and Feature Engineering (27% of the exam)

2.1 Prepare and clean data in Snowflake.

• Use Snowpark for Python and SQL - Aggregate
• Joins
• Identify critical data
• Remove duplicates
• Remove irrelevant fields
• Handle missing values
• Data type casting
• Sampling data

2.1 summary: This section highlights the data preparation steps needed in Snowflake. You will need to demonstrate familiarity with Snowpark for Python and SQL to perform transformations such as joins, aggregations, and deduplication.

Handling missing values, cleaning irrelevant fields, and ensuring proper data types are also covered. This knowledge ensures you can make raw data analysis-ready within the Snowflake platform itself.

2.2 Perform exploratory data analysis in Snowflake.

• Identify patterns with Snowpark and SQL
• Connect external ML platforms or notebooks
• Use Snowflake statistical functions: Window functions, MIN/MAX/AVG/STDEV, VARIANCE, TOPn, approximations
• Perform linear regression calculations

2.2 summary: Exploratory data analysis (EDA) is central to understanding your dataset. Here you focus on using Snowflake-native functions to generate descriptive statistics and reveal early insights.

In addition, you will learn to connect to notebook environments like Jupyter while still leveraging Snowflake’s computing power. The combination of SQL and Snowpark creates efficient, flexible EDA workflows.

2.3 Perform feature engineering on Snowflake data.

• Preprocessing - Scaling, encoding, normalization
• Data transformations using DataFrames
• Derived features (e.g., average spend)
• Binarizing data with binning, label encoding, one hot encoding
• Snowpark Feature Store

2.3 summary: In this section, you deepen your ability to shape features to maximize predictive power. Topics include normalization, scaling, and encoding as core preprocessing tasks, as well as techniques for transforming data into meaningful features.

The use of Snowpark Feature Store is also tested, showing how modern feature management can create scalable, reusable ML workflows. The exam demonstrates the importance of building features that align well with model objectives.

2.4 Visualize and interpret the data to present a business case.

• Use Snowsight with SQL
• Interpret graphs with open-source libraries
• Identify outliers
• Use Snowflake Notebooks

2.4 summary: Data visualization is key to explaining insights. You will practice using Snowsight with SQL queries to summarize datasets, and supplementing with open-source libraries to create compelling visuals.

This section also tests your ability to highlight anomalies and tell a story with data. Snowflake Notebooks provide an integrated analytical environment that supports seamless communication of insights.

Domain 3: Model Development (31% of the exam)

3.1 Connect data science tools directly to data in Snowflake.

• Snowpark
• Snowpark ML
• Python connector with pandas support
• Spark connector
• External IDEs (e.g., Visual Studio Code)
• Snowpark languages

3.1 summary: This section emphasizes connecting Snowflake to the tools data scientists use every day. You will learn to use connectors and APIs with pandas, Spark, and Snowpark ML.

Working with external IDEs extends flexibility, building confidence in using familiar open-source tools while still taking advantage of Snowflake’s data platform.

3.2 Leverage GenAI and LLM models in Snowflake.

• Snowflake Cortex - Vector embedding
• Prompt engineering
• Fine tuning
• Task-specific applications such as summarization or sentiment analysis

3.2 summary: Generative AI and large language models play a growing role in machine learning. This section focuses on Snowflake Cortex capabilities, including embedding vectors and applying prompt engineering.

You’ll also learn to integrate fine tuning workflows and apply models directly for tasks like categorization, summarization, and sentiment scoring. Understanding these techniques helps you align modern AI capabilities with Snowflake data.

3.3 Train a data science model.

• Automate data transformation with dynamic tables
• Python UDFs and UDTFs
• Hyperparameter tuning
• Optimization metric selection (e.g., RMSE, log loss, AUC)
• Cross validation and partitioning
• Sampling strategies
• Training with external functions or stored procedures

3.3 summary: Model training is about building reliability and efficiency into machine learning workflows. You will practice cross validation techniques, hyperparameter optimization, and metric evaluation.

Training with Snowflake-native tools like UDFs, UDTFs, and dynamic tables helps move the iterative process closer to the data layer. The exam emphasizes fluency in these integrations as part of building robust pipelines.

3.4 Validate a data science model.

• Use ROC curves and confusion matrices
• Interpret regression residual plots
• Apply metrics to evaluate accuracy and payouts

3.4 summary: Evaluation metrics ensure models truly represent real-world expectations. This domain tests your ability to interpret ROC curves, confusion matrices, and regression relationships.

You will also need to align quantitative measures with context by reviewing residuals and outcome metrics to determine readiness.

3.5 Interpret a model.

• Feature impact
• Partial dependence plots
• Confidence intervals

3.5 summary: Model explainability is vital in applied machine learning. This section demonstrates interpretability techniques that highlight how input features drive model outcomes.

Using plots and confidence intervals, you connect statistical rigor with practical explanation to stakeholders. Snowflake stored procedures provide streamlined methods to make these insights operational.

Domain 4: Model Deployment (25% of the exam)

4.1 Move a data science model into production.

• External functions and hosted models
• Vectorized and scalar Python UDFs
• Storing predictions and stage commands
• Snowflake Model Registry with logging and container services

4.1 summary: This section focuses on bringing models into production. You will learn deployment best practices for UDFs, container services, and registry features to manage live models.

External function integration extends flexibility and allows hybrid cloud deployment. The key is demonstrating how Snowflake can operationalize models reliably and securely.

4.2 Determine the effectiveness of a model and retrain if necessary.

• Detecting data drift and model decay
• Measurement with AUC, RMSE, accuracy, precision, recall
• UDFs for continuous monitoring

4.2 summary: Monitoring is critical for maintaining model health. This section tests your understanding of identifying model decay and data drift through distributional checks and monitoring metrics.

Retraining strategies and performance validations help keep models aligned with evolving datasets. You will show you can maintain value over time.

4.3 Outline model lifecycle and validation tools.

• Metadata tagging
• Model versioning with Snowflake Model Registry
• Automation of retraining

4.3 summary: Finally, this section emphasizes governance and lifecycle management. Metadata tagging and versioning give control and auditability for deployed models.

Automation of retraining ensures workflows remain dynamic and efficient, highlighting Snowflake’s role in integrated lifecycle management for long-term sustainability.

Who should pursue the Snowflake SnowPro Advanced Data Scientist Certification?

The SnowPro Advanced: Data Scientist Certification is designed for experienced data scientists and machine learning professionals who want to showcase their ability to apply advanced analytics and AI capabilities within Snowflake’s Data Cloud.

You are the ideal candidate if you:

• Have 2+ years of hands-on experience with Snowflake in a production environment.
• Work with Python, R, SQL, PySpark, or Scala for data science workloads.
• Want to demonstrate your ability to train, evaluate, and deploy ML and GenAI models directly within Snowflake.

This certification elevates your profile, setting you apart as someone who can not only perform advanced analytics but also drive enterprise-scale AI innovation on the Snowflake platform.

What career opportunities does the SnowPro Advanced Data Scientist exam unlock?

Earning this certification highlights your ability to lead complex machine learning and AI projects. It opens doors to roles such as:

• Data Scientist (Snowflake-focused)
• Machine Learning Engineer
• AI Engineer
• Applied Scientist
• Advanced Analytics Specialist
• Data Science Consultant

Beyond technical opportunities, this certification can also position you for leadership roles where expertise in generative AI, MLOps, and scalable data science frameworks helps drive data strategy across organizations.

What is the latest exam version and code for this certification?

The current version of the exam is DSA-C03. This is the latest release of the SnowPro Advanced: Data Scientist exam blueprint. As Snowflake evolves its capabilities, especially around GenAI and LLM functionalities, this version reflects those innovations as part of the exam.

By preparing for DSA-C03, you are studying the most up-to-date exam format and aligning with Snowflake’s latest data science best practices.

How long is the SnowPro Advanced Data Scientist exam?

You will have 115 minutes to complete the exam. This time is designed to allow you to carefully work through the 65 exam questions, which include multiple-choice, multiple-select, and interactive formats.

With proper preparation, candidates often find this time sufficient to think critically about scenario-based questions while still pacing themselves comfortably.

How many questions are included on the Snowflake DSA-C03 exam?

The exam consists of 65 questions that test your ability across all four key domains. The questions blend practical scenarios with theoretical knowledge, ensuring you not only understand Snowflake concepts but also how to apply them in enterprise-scale data science projects.

Some questions may be unscored and used for exam research purposes, but you will not be able to tell which they are. This makes it important to give your best effort on every question.

What is the passing score for the Snowflake SnowPro Advanced Data Scientist exam?

To pass, you’ll need a scaled score of 750 out of 1000. Because Snowflake uses scaled scoring, this means even if you are weaker in certain domains, strong performance in others can balance out.

This scoring approach rewards a solid overall understanding of data science concepts and Snowflake capabilities without penalizing you for small knowledge gaps.

How much does the exam cost to take?

The exam fee is $375 USD worldwide, with a discounted price of $300 USD for those registering in India.

When considering the potential career advantage and credibility this certification brings, it’s a powerful investment in your career growth.

What language is the Snowflake SnowPro Advanced Data Scientist exam available in?

The exam is available in English only. Candidates preparing globally should be comfortable with technical terminology in English, particularly around machine learning and analytics.

What knowledge areas should I focus on while preparing?

The certification exam blueprint breaks down into four domains:

1. Data Science Concepts (17%)

   • Machine learning fundamentals and supervised vs. unsupervised learning
   • ML lifecycle, evaluation techniques, and statistical principles

2. Data Preparation and Feature Engineering (27%)

   • Cleaning, aggregating, and transforming data in Snowflake
   • Exploratory analysis with SQL and Snowpark
   • Normalization, encoding, and feature creation

3. Model Development (31%)

   • Training ML models in Snowflake with Snowpark and external tools
   • Leveraging Snowflake Cortex for LLMs, embeddings, and fine-tuning
   • Hyperparameter tuning, pipelines, metrics, and interpretability

4. Model Deployment (25%)

   • Deploying ML/GenAI models via UDFs and Snowflake Model Registry
   • Handling model drift, automation of retraining, and lifecycle management

Snowflake emphasizes practical understanding of model management, feature engineering, and advanced ML workflows rather than just theory.

How difficult is the DSA-C03 exam?

This is an advanced certification, but with solid practical experience and structured study, it is very attainable. It evaluates your skills in applying data science to real-world Snowflake environments.

If you have already worked with Snowpark, Snowflake ML functions, and model deployment strategies, you’ll find the scenarios highly relevant to your daily work.

What types of questions appear on the exam?

The exam includes:

• Multiple-choice questions (one correct answer)
• Multiple-select questions (two or more correct answers)
• Interactive scenario-style questions that test practical application

These formats are designed to show your actual problem-solving and analytical approach, rather than simple memorization.

Is there a prerequisite before taking the SnowPro Advanced Data Scientist exam?

Yes. To register for this exam, you must hold an active SnowPro Core Certification. The SnowPro Core ensures all advanced test takers have an established foundation in Snowflake’s architecture and ecosystem.

How is the exam structured across different topics?

The exam domains are weighted as follows:

• Data Science Concepts – 17%
• Data Preparation and Feature Engineering – 27%
• Model Development – 31%
• Model Deployment – 25%

This weighting highlights the importance of being hands-on with model development and feature engineering, which together form well over half of the exam.

Where can I take the exam?

You have two options for delivery:

• Online proctored exam — take it from your home or office with a stable internet connection.
• Onsite testing center — sit for the exam at an authorized Pearson VUE testing facility.

Both methods are equally recognized, so you can choose whichever environment helps you perform most confidently.

What are common mistakes candidates make and how can I avoid them?

Some candidates underestimate key areas such as:

• Feature Engineering — Proper handling of missing values, encoding, and scaling is frequently tested.
• Model Deployment Concepts — Many overlook UDFs, external functions, and registry details.
• GenAI and LLM Integration — Since Snowflake Cortex is newer, candidates sometimes skip preparing for embedding and fine-tuning workflows.

The best approach is to balance your study across all four domains while gaining hands-on practice with Snowpark and Snowflake ML APIs.

How long does the SnowPro Advanced Data Scientist certification remain valid?

This certification remains valid for 2 years from the date of issue.

To renew, you can either:

• Earn another eligible SnowPro Advanced certification.
• Take approved Snowflake Instructor-Led Training (ILT) courses.
• Recertify through Snowflake’s Continuing Education program.

This ensures credential holders are always aligned with Snowflake’s evolving capabilities.

How can I prepare effectively for the DSA-C03 Snowflake certification?

A structured preparation plan is key. Recommendations include:

• Instructor-led Data Science Training from Snowflake
• Self-study using Snowflake documentation and technical blogs
• Hands-on labs and experiments with machine learning inside Snowflake
• Reviewing statistical concepts and ML lifecycle best practices

To boost your confidence, we highly recommend using SnowPro Advanced Data Scientist practice exams with full explanations. These mock tests simulate the real exam experience and help you strengthen weak areas before taking the official exam.

How does this certification fit into a long-term career path?

This certification doesn’t just validate Snowflake skills; it positions you as a forward-thinking data scientist capable of driving enterprise AI transformation.

It can complement or precede other advanced SnowPro certifications such as Data Engineer, Architect, or Administrator, giving you an elite specialization that matches your career goals.

What special Snowflake capabilities are emphasized on the exam?

This certification highlights modern and in-demand data science approaches:

• Snowpark APIs for Python, SQL, and Scala
• Feature Store for scalable ML feature management
• Cortex GenAI framework for embeddings, prompts, and fine-tuning
• Snowflake Model Registry for governance, versioning, and monitoring
• Deployment via UDFs, external functions, and containerized services

By mastering these, you become proficient in running full ML and AI workflows directly on Snowflake’s AI Data Cloud.

How do I register for the Snowflake SnowPro Advanced Data Scientist Certification exam?

It’s simple to register and begin your certification journey:

1. Visit the official Snowflake SnowPro Advanced Data Scientist Certification page.
2. Select “Register Now” and log into the Certification Management system.
3. Pick your preferred test method (online or in-person).
4. Choose a date, complete your registration, and get ready to excel!

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The Snowflake SnowPro Advanced Data Scientist Certification is a fantastic way to display your mastery of AI, ML, and data science capabilities in the Snowflake ecosystem. Earning this credential demonstrates not only your advanced technical expertise but also your ability to thrive in the future of enterprise AI and analytics. With steady preparation, hands-on labs, and structured study guides, you’ll be ready to pass the exam and accelerate your career to new heights.