Free Practice Questions for Snowflake DSA-C03 Certification
- Exam guide version:
- January 12, 2026
- 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 Scientist (DSA-C03). All questions are aligned with the latest exam guide and include detailed explanations to help you master the material.
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16 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
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Exam Details
Key information about Snowflake SnowPro Advanced: Data Scientist (DSA-C03)
- Multiple choice
associate (intermediate)
Snowflake Continuing Education (CE) program (eligible ILT Training Courses, equivalent or higher-level SnowPro Certification)
January 12, 2026
Active SnowPro Core Certified credential
2+ years of practical data science experience with Snowflake in an enterprise environment; Data Scientists, AI or ML Engineers
10 – 13 hours
2 years
Exam Topics & Skills Assessed
Skills measured (from the official study guide)
1: Data Science Concepts
1.1: Define machine learning concepts for data science workloads.
● Machine Learning ○ Supervised learning ○ Unsupervised learning ○ Reinforcement learning
1.2: Identify machine learning problem types.
● Supervised Learning ○ Structured Data ■ Linear regression ■ Binary classification ■ Multi-class classification ■ Time-series forecasting ○ Unstructured Data ■ Image classification ■ Segmentation ● Unsupervised Learning ○ Clustering ● GenAI ○ Association models
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
1.4: Define statistical concepts for data science.
● Normal versus skewed distributions (e.g., mean, outliers) ● Central limit theorem ● Z and T tests ● Bootstrapping ● Confidence intervals
2: Data Preparation and Feature Engineering
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
2.2: Perform exploratory data analysis in Snowflake.
● Snowpark and SQL ○ Identify initial patterns (i.e., data profiling) ○ Connect external machine learning platforms and/or notebooks (e.g., Jupyter) ● Use Snowflake native statistical functions to analyze and calculate descriptive data statistics. ○ Window Functions ○ MIN/MAX/AVG/STDEV ○ VARIANCE ○ TOPn ○ Approximation/High Performing function ● Linear Regression ○ Find the slope and intercept ○ Verify the dependencies on dependent and independent variables
2.3: Perform feature engineering on Snowflake data.
● Preprocessing ○ Scaling data ○ Encoding ○ Normalization ● Data Transformations ○ DataFrames (i.e., pandas, Snowpark, Snowpark pandas) ○ Derived features (e.g., average spend) ● Binarizing data ○ Binning continuous data into intervals ○ Label encoding ○ One hot encoding ● Snowpark Feature Store
2.4: Visualize and interpret the data to present a business case.
● Statistical summaries ○ Snowsight with SQL ○ Interpret open-source graph libraries ○ Identify data outliers ● Snowflake Notebooks
3: Model Development
3.1: Connect data science tools directly to data in Snowflake.
● Connecting Python to Snowflake ○ Snowpark ○ Snowpark ML ○ Python connector with Pandas support ● Connecting from external IDE (e.g., Visual Studio Code) ● Snowpark languages
3.2: Leverage GenAI and LLM models in Snowflake.
● Snowflake Cortex ○ Vector embedding ○ Prompt engineering ○ Fine tuning ○ Task-specific models (e.g., categorization, summarization, sentiment analysis, information extraction)
3.3: Train a data science model.
● Build a data science pipeline ○ Automation of data transformation (e.g., dynamic tables) ○ Python User-Defined Functions (UDFs) ○ Python User-Defined Table Functions (UDTFs) ○ Python stored procedures ● Hyperparameter tuning ● Optimization metric selection (e.g., log loss, AUC, RMSE) ● Partitioning ○ Cross validation ○ Train validation hold-out ● Down/up-sampling ● Training with Python stored procedures ● Training outside Snowflake through external functions ● Training with Python User-Defined Table Functions (UDTFs)
3.4: Validate a data science model.
● ROC curve/confusion matrix ○ Calculate the expected payout of the model ● Regression problems ● Residuals plot ○ Interpret graphics with context ● Model metrics
3.5: Interpret a model.
● Feature impact ● Partial dependence plots ● Confidence intervals ● SHAP values
4: Model Deployment
4.1: Move a data science model into production.
● Use an external hosted model ○ External functions ○ Pre-built models ● Deploy a model in Snowflake ○ Vectorized/Scalar Python User-Defined Functions (UDFs) ○ Pre-built models ○ Storing predictions ○ Stage commands ○ Snowflake Model Registry ■ Model logging and retrieving ■ Snowpark Container Services
4.2: Determine the effectiveness of a model and retrain if necessary.
● Metrics for model evaluation ○ Data drift /Model decay ■ Data distribution comparisons (Do the data making predictions look similar to the training data? Do the same data points give the same predictions once a model is deployed?) ● Area under the curve ● Accuracy, precision, recall ● RMSE (regression)
4.3: Outline model lifecycle and validation tools.
● Metadata tagging ● Model versioning with Snowflake Model Registry ● Automation of model retraining
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