Databricks Machine Learning Associate study guide
Databricks Machine Learning Associate · Work through your exam, one topic at a time.
The Databricks Certified Machine Learning Associate is a proctored certification that assesses an individual's ability to use Databricks to perform basic machine learning tasks. Per Databricks, the exam tests understanding of the platform and its machine learning capabilities - including AutoML, Unity Catalog, and select features of MLflow - as well as data exploration, feature engineering, and model building through training, tuning, evaluation and selection, plus model deployment. It is aimed at practitioners with roughly six or more months of hands-on experience performing the tasks outlined in the exam guide; there are no formal prerequisites, though related training is highly recommended. All machine learning code on the exam is Python, while data manipulation code in non-ML workflow contexts may be provided in SQL. The Databricks Machine Learning Associate study guide has 4 sections and about 1,241 words.
01Databricks Machine Learning: Platform Fundamentals
The Databricks Machine Learning domain is the platform-literacy section: compute, workspace objects, and governance primitives you will lean on in every other domain. Know the difference between an all-purpose cluster (long-lived, interactive, shared by a team, terminated manually or by policy) and a job cluster (created for a single job run, isolated, cheaper, destroyed at the end).
316 words, about 2 min02ML Workflows: Tracking, Registry, and Feature Store
Every training attempt is a run inside an experiment . You log params (hyperparameters, one value per key per run), metrics (numeric, optionally with a step for curves), tags (free text), and artifacts (files, plots, models).
304 words, about 2 min03Model Development: Features, Training, and Tuning
Spark ML models consume a single vector column, so numeric features are combined with VectorAssembler . Categorical strings go through StringIndexer then OneHotEncoder ; scale with StandardScaler or MinMaxScaler when the algorithm is distance- or coefficient-sensitive.
279 words, about 1 min04Model Deployment: Batch, Streaming, and Real-Time Serving
Batch : a scheduled job loads data, applies the model with mlflow.pyfunc.spark udf() (or the feature store's score batch ), and writes predictions to a Delta table. Streaming : the same scoring logic inside foreachBatch or writeStream .
342 words, about 2 min