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Databricks Certified Machine Learning Associate Syllabus

Databricks Machine Learning

Using Databricks ML tooling: AutoML, feature tables, MLflow and the UC model registry.

38%
Weight
18
Questions
38
Marks

AutoML and Runtime

  • MLOps best practices
  • Benefits of Databricks Runtime for ML
  • AutoML for model and feature selection
  • AutoML-generated notebooks

Feature Engineering in Unity Catalog

  • Account-level vs. workspace-level feature store benefits
  • Creating and writing feature tables
  • Training and scoring with feature tables
  • Online vs. offline feature tables

MLflow and Model Registry

  • Finding the best run with the MLflow Client API
  • Manually logging metrics, artifacts and models
  • Registering models in Unity Catalog
  • Champion/challenger aliases and tags
  • Code promotion vs. model promotion

Data Processing

Exploring, cleaning and transforming features for modeling.

19%
Weight
9
Questions
19
Marks

Exploration

  • Summary statistics with .summary() and dbutils
  • Visualizing categorical and continuous features
  • Outlier removal with standard deviation or IQR
  • Comparing distributions of categorical features

Feature Preparation

  • Imputing with mean, median or mode
  • One-hot encoding use cases
  • Log-scale transformations
  • Comparing imputation methods

Model Development

Selecting, tuning and evaluating models.

31%
Weight
15
Questions
31
Marks

Algorithms and Pipelines

  • Algorithm selection from ML foundations
  • Mitigating class imbalance
  • Estimators vs. transformers
  • Building training pipelines

Tuning and Validation

  • Hyperopt fmin
  • Random, grid and Bayesian search
  • Cross-validation vs. train-validation split
  • Counting models trained in grid search with CV
  • Parallelizing single-node model tuning

Evaluation

  • F1, log loss and ROC/AUC
  • RMSE, MAE and R-squared
  • Exponentiating log-transformed predictions
  • Bias-variance tradeoff

Model Deployment

Serving models for batch, streaming and real-time inference.

12%
Weight
6
Questions
12
Marks

Serving Approaches

  • Batch vs. streaming vs. real-time serving
  • Batch inference with pandas
  • Streaming inference with Lakeflow Declarative Pipelines
  • Choosing an approach for latency needs

Model Serving Endpoints

  • Deploying custom models to Model Serving endpoints
  • Querying endpoints for real-time inference
  • Traffic splitting between endpoints
  • Serving models registered in Unity Catalog

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Databricks Machine Learning 85%
Data Processing 92%

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