Databricks-ML-Assoc ML Workflows Practice Question
A data scientist is training a model using MLflow on Databricks. They need to ensure that the model artifacts and metrics are logged automatically without adding manual logging code to the training script. Which approach should they use?
⚠ Common exam trap
Students mistakenly think autologging happens automatically without code invocation, forgetting they must explicitly call 'mlflow.autolog()' within the notebook.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Execute mlflow.autolog() at the start of the notebook cell before running the model training code.
MLflow provides autologging capabilities that automatically capture parameters, metrics, and models when using popular machine learning libraries like Scikit-learn, TensorFlow, or PyTorch. By calling mlflow.autolog() before the training code, the framework instruments the library calls to record telemetry automatically. This approach minimizes boilerplate code and ensures consistency across experiments, which is essential for auditability and model reproducibility in collaborative Databricks environments, as it prevents human error in manual logging.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Call mlflow.set_tracking_uri() inside the training function to redirect logs to the workspace.
Why it's wrong here
Setting the tracking URI only points the client to the MLflow tracking server location. It does not initiate the automatic collection of training metrics or model artifacts from library-specific training functions. This command is necessary for remote tracking but fails to solve the requirement of automated, code-free logging of model parameters.
- ✗
Wrap the training logic within an mlflow.start_run() block without any additional configuration.
Why it's wrong here
Starting an MLflow run creates a container for logging, but it does not automatically extract parameters or metrics from training libraries. Without explicitly calling autologging or manual logging functions, the run will record nothing more than empty metadata, failing to capture the required model artifacts and training performance metrics.
- ✓
Execute mlflow.autolog() at the start of the notebook cell before running the model training code.
Why this is correct
Calling mlflow.autolog() enables the library-specific hooks that automatically record metrics, parameters, and artifacts during the execution of supported model training methods. This is the standard practice for Databricks ML workflows to reduce manual instrumentation overhead while ensuring that every model iteration is fully documented and tracked automatically.
- ✗
Configure the Databricks cluster environment variable MLFLOW_TRACKING_ENABLED to true.
Why it's wrong here
While enabling tracking is a prerequisite, this environment variable simply ensures the MLflow client can communicate with the server. It does not trigger the instrumentation required to inspect library-specific training objects, meaning no metrics or model artifacts would be captured during the training loop without the autologging call.
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Last reviewed September 2026 · checked against the official Databricks exam blueprint
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