Databricks Certified Machine Learning Associate

Last checked October 22, 2026 against the official Databricks exam page.

1. Question 1Databricks Machine Learning

   A data scientist wants each training run's parameters, metrics and model artifacts recorded so that runs in a workspace can be compared later. Which tool provides this?

   1. AUnity Catalog lineage, which records which tables and notebooks a particular query has read
   2. BDelta Lake time travel, which restores an earlier version of a table to a previous state
   3. CMLflow Tracking, which logs parameters, metrics and artifacts for each run in an experiment
   4. DDatabricks Connect, which runs local Python code against a remote cluster for interactive work
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   Correct answer: C

   MLflow Tracking records parameters, metrics and artifacts for every run in an experiment, so runs can be compared in the workspace. Delta time travel and Unity Catalog lineage concern tables and data access rather than model runs. Databricks Connect runs code remotely and does not record experiment results.

2. Question 2Model Development

   A binary classifier is trained on data where the positive class is very rare. Which evaluation metric is most informative?

   1. AAccuracy, which measures the share of all predictions that match the true label
   2. BMean squared error, which measures the average squared difference between the predicted and actual values
   3. CArea under the precision-recall curve, which focuses on how well the rare positive class is found
   4. DR-squared, which measures the share of variance explained, for continuous regression targets
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   Correct answer: C

   With a rare positive class, accuracy can look high even when the model misses most positives, while the precision-recall curve shows how well positives are found. Mean squared error and R-squared are regression measures and do not suit a binary classifier.

3. Question 3Data Processing

   A dataset has missing values in a numeric column, and the team wants to fill them with the median inside a Spark ML pipeline. Which transformer does this?

   1. AStandardScaler, which scales each value to unit variance around the mean of the column
   2. BBucketizer, which assigns each value to one of a fixed set of ranges defined by split points
   3. COneHotEncoder, which creates a binary column for each distinct value in a categorical column
   4. DImputer, which replaces missing values with the mean or median of each column
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   Correct answer: D

   The Imputer transformer fills missing values with the mean or median of each column, which matches the requirement. StandardScaler rescales values, OneHotEncoder encodes categories, and Bucketizer groups values into ranges. None of them fills missing values.

4. Question 4Model Deployment

   A team wants predictions for every row of a large table written to a Delta table each night. Which deployment pattern is appropriate?

   1. AA real-time serving endpoint that the nightly job calls once for each individual row of the table
   2. BA dashboard that scores each row again every time a user opens the page in the browser
   3. CA notebook that a person runs manually whenever a prediction is needed for a customer request
   4. DBatch inference that loads the registered model and writes the predictions to a Delta table
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   Correct answer: D

   Batch inference scores the whole table in one job and writes predictions to a Delta table, which suits a nightly schedule. Calling a real-time endpoint row by row is slow and costly at that volume. Manual notebooks and dashboards do not produce a reliable nightly output.

5. Question 5Databricks Machine Learning

   A team wants Databricks to train and compare several candidate models on a labelled table, then generate notebooks containing the code for each candidate. Which feature does this?

   1. ADatabricks Repos, which syncs notebooks with a remote Git repository for version control
   2. BAutoML, which trains and compares candidate models and generates notebooks
   3. CDatabricks SQL warehouses, which serve queries to dashboards and BI tools for analysts
   4. DUnity Catalog volumes, which store unstructured files such as images for later training
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   Correct answer: B

   AutoML trains and compares candidate models on a dataset and generates notebooks that reproduce each one, which saves the team from writing the baseline pipelines by hand. Repos handles source control, SQL warehouses serve queries, and volumes store files. None of them trains and compares candidate models.