Databricks Certified Machine Learning Associate

Identify MLOps best practices and advantages of ML runtimes. Identify how AutoML facilitates model and feature selection and advantages it brings to model development. Identify benefits of creating feature store tables at the account level in Unity Catalog vs at the workspace level. Create a feature store table in Unity Catalog, write data to it, train a model with features from it, and score a model using features from it. Describe differences between online and offline feature tables. Identify the best run using the MLflow Client API. Manually log metrics, artifacts, and models in an MLflow run. Identify information available in the MLflow UI. Register a model using the MLflow Client API in the Unity Catalog registry. Identify benefits of registering models in the Unity Catalog registry over the workspace registry. Identify scenarios where promoting code is preferred over promoting models and vice versa. Set or remove a tag for a model. Promote a challenger model to a champion model using aliases.

Compute summary statistics on a Spark DataFrame using .summary() or dbutils data summaries. Remove outliers from a Spark DataFrame based on standard deviation or IQR. Create visualizations for categorical or continuous features. Compare two categorical or two continuous features using the appropriate method. Compare and contrast imputing missing values with mean, median, or mode. Impute missing values with mode, mean, or median. Use one-hot encoding for categorical features. Identify and explain model types or datasets for which one-hot encoding is or is not appropriate. Identify scenarios where log scale transformation is appropriate.

Use ML foundations to select the appropriate algorithm for a given model scenario. Identify methods to mitigate data imbalance in training data. Compare estimators and transformers. Develop a training pipeline. Use Hyperopt fmin operation to tune a model's hyperparameters. Perform random or grid search or Bayesian search for hyperparameter tuning. Parallelize single-node models for hyperparameter tuning. Describe benefits and downsides of cross-validation over a train-validation split. Perform cross-validation as part of model fitting. Identify the number of models being trained in conjunction with grid-search and cross-validation. Use common classification metrics: F1, Log Loss, ROC/AUC. Use common regression metrics: RMSE, MAE, R-squared. Choose the most appropriate metric for a given scenario objective. Identify the need to exponentiate log-transformed variables before calculating evaluation metrics or interpreting predictions. Assess the impact of model complexity and the bias-variance tradeoff on model performance.

Identify the differences and advantages of model serving approaches: batch, realtime, and streaming. Deploy a custom model to a model endpoint. Use pandas to perform batch inference. Identify how streaming inference is performed with Delta Live Tables. Deploy and query a model for realtime inference. Split data between endpoints for realtime inference.