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.

Snowflake Data Scientist Certification DSA-C02 Exam!!

Why take this course?