Beginner

Exam Overview

Everything you need to know about the Snowflake Machine Learning Specialization certification — exam format, domains, cost, prerequisites, study plans, and the registration process.

Exam At a Glance

The Snowflake Machine Learning Specialization certification validates your ability to build, deploy, and manage machine learning solutions within the Snowflake Data Cloud. It covers Snowpark for ML, feature engineering, model training, and model deployment using Snowflake-native tools and integrations.

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Key facts: 65 questions • 115 minutes • Scaled scoring • $175 USD • Valid for 2 years • Online proctored via Kryterion

Question Format

The exam consists of two question types:

  • Multiple choice — One correct answer out of four options (majority of questions)
  • Multiple select — Two or more correct answers out of five options (clearly marked as "select ALL that apply")

There is no penalty for guessing. Never leave a question blank. If you are unsure, eliminate what you can and make your best choice.

Scoring

Snowflake uses a scaled scoring model. The minimum passing score is approximately 750 out of 1000. The scaled scoring accounts for question difficulty. In practice, most candidates report needing roughly 70-75% of questions correct to pass.

Some questions may be unscored (used for statistical evaluation of future exams). Treat every question as if it counts.

Exam Domains

The exam is organized around four core domains. Understanding these domains and their relative weights is critical for prioritizing your study time.

Domain 1: Snowpark for ML

Snowpark Python API, DataFrames, UDFs, vectorized UDFs, stored procedures for ML workloads, session management, and Snowpark environment setup.

Domain 2: Feature Engineering

SQL-based feature transforms, Snowpark feature pipelines, Feature Store, data preparation techniques, and handling time-series and categorical data within Snowflake.

Domain 3: Model Training

Snowpark ML library, built-in ML functions, training with scikit-learn, XGBoost, LightGBM, and PyTorch inside Snowflake, hyperparameter tuning, and cross-validation.

Domain 4: Model Deployment & Serving

Snowpark Container Services, model registry, UDF-based inference, batch inference with tasks, model versioning, monitoring, and production ML patterns in Snowflake.

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Study priority: Snowpark for ML and Model Training are heavily tested. If you are short on time, prioritize hands-on experience with the Snowpark Python API, the snowflake-ml-python library, and model registry workflows.

Prerequisites

Snowflake recommends the following background before attempting the exam:

  • SnowPro Core Certification (recommended but not required)
  • 6+ months of experience using Snowflake for data engineering or analytics
  • Familiarity with Python and data science libraries (pandas, scikit-learn, NumPy)
  • Understanding of machine learning fundamentals (supervised/unsupervised learning, evaluation metrics)
  • Experience with SQL for data manipulation and transformation

4-Week Accelerated Study Plan

For experienced Snowflake practitioners who want to move quickly:

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Week 1: Snowpark Python deep dive — DataFrames, UDFs, vectorized UDFs, stored procedures
Week 2: Feature engineering in Snowflake — SQL transforms, Feature Store, data pipelines
Week 3: Model training with snowflake-ml-python — preprocessing, modeling, hyperparameter tuning
Week 4: Model deployment + Snowpark Container Services + practice exam + review weak areas

6-Week Comprehensive Study Plan

For those newer to Snowflake ML or wanting thorough preparation:

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Weeks 1-2: Snowpark fundamentals — environment setup, Python API, DataFrame operations, UDFs, stored procedures
Week 3: Feature engineering — SQL transforms, Snowpark pipelines, Feature Store, time-series features
Week 4: Model training — snowflake-ml-python library, scikit-learn/XGBoost in Snowflake, evaluation metrics
Week 5: Model deployment — Snowpark Container Services, model registry, UDF inference, batch scoring
Week 6: Practice exam, review all weak areas, exam day preparation

Registration Process

  1. Create or sign in to your Snowflake Education account
  2. Navigate to the Certifications section and select Machine Learning Specialization
  3. Schedule your exam through the Kryterion online proctoring platform
  4. Choose your preferred date and time (available 24/7 with online proctoring)
  5. Pay the $175 USD exam fee
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Pro tip: Review the official Snowflake exam guide and try the free sample questions provided by Snowflake before scheduling your exam. This gives you a feel for the question style and difficulty level.

What Is Next

Now that you understand the exam format and have a study plan, it is time to dive into the first domain. In the next lesson, we cover Snowpark for ML — the Snowpark Python API, DataFrame operations, UDFs, and stored procedures for ML workloads, with practice questions to test your knowledge.