Snowflake Data Scientist Certification DSA-C02 Exam 2024!!

Based on the detailed curriculum you've outlined, here's a comprehensive overview of the Snowflake Data Science and Machine Learning certification exam content: Exam Content Overview:

Domain 1.0: Data Understanding

● Statistical concepts for data science

• Normal versus skewed distributions
• Mean, outliers
• Central limit theorem
• Z and T tests
• Bootstrapping
• Confidence intervals

Domain 2.0: Data Pipelining

● Enriching data by consuming data sharing sources

• Snowflake Marketplace
• Direct Sharing
• Shared database considerations

● Building a data science pipeline

• Automation of data transformation with streams and tasks
• Python User-Defined Functions (UDFs)
• Python User-Defined Table Functions (UDTFs)
• Python stored procedures
• Integration with machine learning platforms

Domain 3.0: Data Preparation and Feature Engineering

● Preparing and cleaning data in Snowflake

• Use of Snowpark for Python and SQL
• Data type casting, sampling data, removing duplicates, irrelevant fields, handling missing values

● Data drift /Model decay

● Data distribution comparisons

● Training a data science model

• Hyperparameter tuning
• Optimization metric selection (e.g., log loss, AUC, RMSE)
• Partitioning (e.g., Cross validation, Train validation hold-out)
• Down/Up-sampling

● Validating 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

● Interpreting a model

• Feature impact
• Partial dependence plots
• Confidence intervals

Domain 4.0: Model Deployment

● Moving a data science model into production

• Use of an external hosted model (e.g., External functions, Pre-built models)
• Deploying a model in Snowflake (e.g., Vectorized/Scalar Python User Defined Functions (UDFs), Pre-built models, Storing predictions)
• Determining the effectiveness of a model and retraining if necessary

● Outlining model lifecycle and validation tools

• Streams and tasks
• Metadata tagging
• Model versioning with partner tools
• Automation of model retraining

Candidates are expected to demonstrate proficiency in Snowflake's data science and machine learning features and capabilities. The certification exam will test knowledge and expertise across these domains: understanding data, building a data science pipeline, preparing and transforming data, feature engineering, model deployment, and outlining model lifecycle and validation tools within the Snowflake platform. Candidates should be well-versed in both theoretical and practical aspects of Data Science and Machine Learning as it pertains to the Snowflake platform.