Free Practice Questions for Snowflake DSA-C03 Certification

● Machine Learning ○ Supervised learning ○ Unsupervised learning ○ Reinforcement learning

● 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

● 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

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

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

● 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

● 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

● Statistical summaries ○ Snowsight with SQL ○ Interpret open-source graph libraries ○ Identify data outliers ● Snowflake Notebooks

● Connecting Python to Snowflake ○ Snowpark ○ Snowpark ML ○ Python connector with Pandas support ● Connecting from external IDE (e.g., Visual Studio Code) ● Snowpark languages

● Snowflake Cortex ○ Vector embedding ○ Prompt engineering ○ Fine tuning ○ Task-specific models (e.g., categorization, summarization, sentiment analysis, information extraction)

● 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)

● ROC curve/confusion matrix ○ Calculate the expected payout of the model ● Regression problems ● Residuals plot ○ Interpret graphics with context ● Model metrics

● Feature impact ● Partial dependence plots ● Confidence intervals ● SHAP values

● 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

● 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)

● Metadata tagging ● Model versioning with Snowflake Model Registry ● Automation of model retraining