Exam Prep – Databricks Certified Machine Learning Associate
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Learning Track
Exam Prep
Delivery methods
On-Site, Virtual
Duration
1 day
This course adopts a question-driven preparation approach tailored to the Databricks Certified Machine Learning Associate exam using the official March 1 2025 exam guide. Each module is anchored by realistic exam-style practice questions aligned with official domains. Instructors guide learners through every answer option—explaining correct reasoning and why distractors are wrong—with focused mini-lectures or demos as needed.
Learning Objectives
By the end of this course, learners will be able to:
- Accurately interpret and approach exam-style questions across all domains.
- Understand reasoning behind both correct answers and common pitfalls.
- Apply Databricks ML tools effectively: AutoML, Feature Store, MLflow, Unity Catalog, model training/tuning, and deployment.
- Build strategic pacing and exam-day confidence through repeated question practice.
Audience
- Candidates preparing for the Databricks Certified Machine Learning Associate exam
- Machine learning practitioners with ~6 months of Databricks ML experience
- Learners who favor a practice-first learning style
Prerequisites
- Working knowledge of Python and core ML libraries (e.g., scikit-learn, Spark ML)
- Familiarity with Databricks Workspace, Unity Catalog, and basic ML operations
- Experience with model training, tuning, evaluation, and deployment workflows
Course outline
- Certification format, domains, scoring, timing
- Strategies for analyzing multiple-choice questions
- Question types and common distractors
- Pacing strategies and flagging questions for review
- MLOps best practices and ML Runtime advantages
- AutoML’s role in feature/model selection and its benefits
- Unity Catalog Feature Store: Benefits, creating tables, writing data, model training and scoring
- Differences between online and offline feature tables
- MLflow: Identifying best runs, logging metrics/artifacts/models, UI insights, model registration in Unity Catalog, tagging, and champion model promotion
- Generating summary statistics using .summary() or dbutils
- Outlier removal via standard deviation or IQR
- Creating visualizations for categorical and continuous features
- Comparing feature types and imputation techniques: mean, median, mode
- When to use one-hot encoding or log transformation
- Choosing algorithms using ML foundations
- Techniques for handling data imbalance
- Comparing estimators vs. transformers, designing training pipelines
- Hyperparameter tuning: Hyperopt’s fmin, random/grid/Bayesian search, parallelization
- Cross-validation vs. train-validation split: trade-offs, implementation
- Classification metrics (F1, Log Loss, ROC/AUC) and regression metrics (RMSE, MAE, R²)
- Interpreting log-transformed outputs and understanding the bias-variance trade-off
- Serving approaches: Batch, real-time, streaming
- Deploying custom models to endpoints
- Batch inference via pandas
- Streaming inference using Delta Live Tables
- Deploying and querying real-time endpoints, including traffic splitting between endpoints
