Databricks-ML-Assoc Databricks Machine Learning Practice Question
A data scientist is using MLflow to train a scikit-learn model on Databricks. They call mlflow.sklearn.autolog() before fitting the model. After the run completes, they need to retrieve the automatically logged model and load it for batch inference in a separate notebook. Which approach correctly retrieves the logged model for loading?
⚠ Common exam trap
The trap here is assuming that autologging stores models in a fixed DBFS location or a Delta table, when it actually logs them as artifacts tied to the run ID.
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
✓
Use the MLflow run ID to construct the artifact URI 'runs:/<run_id>/model' and pass it to mlflow.sklearn.load_model().
With MLflow autologging, the trained model is saved as an artifact in the run's artifact directory under the name 'model'. To load it later, you need the run ID and the artifact path. The 'runs:/' URI scheme provides a portable reference to the run's artifacts, and mlflow.sklearn.load_model() correctly interprets that URI to reconstruct the model.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Access the model directly from the DBFS path '/dbfs/FileStore/models/<run_id>' and load it using joblib.load().
Why it's wrong here
autolog does not store models in a fixed DBFS location like '/dbfs/FileStore/models/<run_id>'. It logs artifacts relative to the run's artifact URI, which is typically under the experiment's artifact location. There is no guarantee that path exists, and joblib.load() would not resolve the MLflow model format correctly.
- ✗
Read the model from the Delta table that MLflow automatically creates in the default database when autologging is enabled.
Why it's wrong here
MLflow autologging does not create a Delta table in the default database to store models. It logs artifacts to the run's artifact location and metrics/params to the tracking server. There is no automatic Delta table creation for model artifacts, so this approach would fail because no such table exists.
- ✓
Use the MLflow run ID to construct the artifact URI 'runs:/<run_id>/model' and pass it to mlflow.sklearn.load_model().
Why this is correct
mlflow.sklearn.autolog() logs the trained model to the run's artifact path under 'model'. The run ID uniquely identifies the run, and the artifact URI 'runs:/<run_id>/model' is the standard way to reference that logged model. Passing it to mlflow.sklearn.load_model() correctly loads the model for inference.
- ✗
Query the MLflow tracking server's REST API endpoint '/api/2.0/mlflow/runs/get' and extract the model binary from the response.
Why it's wrong here
The MLflow REST API's 'runs/get' endpoint returns metadata about the run, including parameters, metrics, and artifact URIs, but not the model binary itself. To download the model, you must use the artifact URI with the appropriate client or load_model function; the REST API does not embed the model file in its JSON response.
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JA
Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official Databricks exam blueprint
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