Databricks-ML-Pro Model Development Practice Question
An ML engineer is training an XGBoost model on Databricks and wants to leverage hyperparameter tuning using Hyperopt while automatically logging all trial parameters, metrics, and models to MLflow. Which built-in MLflow function should be used to achieve this automatic integration?
⚠ Common exam trap
Candidates often select manual logging methods or generic MLflow functions instead of the specific library-native autologging function, failing to realize that autologging is the most efficient way to capture Hyperopt trials.
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
✓
mlflow.xgboost.autolog()
mlflow.xgboost.autolog() automatically logs parameters, metrics, and trained artifacts during XGBoost training routines without requiring manual logging statements inside the training loop. This function streamlines model development workflows by ensuring complete lineage tracking and experiment reproducibility across extensive hyperparameter tuning sweeps.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
mlflow.xgboost.autolog()
Why this is correct
`mlflow.xgboost.autolog()` hooks directly into XGBoost's training callbacks, capturing each Hyperopt trial's parameters, metrics and resulting model into MLflow runs without manual logging code. This satisfies the stem's requirement for automatic trial logging during hyperparameter tuning, whereas generic `mlflow.autolog()` covers fewer framework-specific details.
- ✗
mlflow.spark.autolog()
Why it's wrong here
mlflow.spark.autolog() hooks into Spark MLlib estimator fitting, not XGBoost training, so Hyperopt trials would log nothing. It is intended for Spark ML pipelines and would be correct only when tuning a Spark MLlib model, not an XGBoost booster.
- ✗
mlflow.sklearn.autolog()
Why it's wrong here
XGBoost models are not scikit-learn estimators, so mlflow.sklearn.autolog() captures no trial parameters or metrics from Hyperopt runs. It is designed for native scikit-learn fit() calls, which is why it would be the right choice only when training a scikit-learn model rather than an XGBoost booster.
- ✗
mlflow.register_model()
Why it's wrong here
mlflow.register_model() only registers an already-created model version in the Model Registry; it captures no parameters, metrics, or trial artefacts during Hyperopt runs. It is the right call after training, when promoting a logged model to a registry stage, not for automatic trial logging.
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Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official Databricks exam blueprint
This Databricks-ML-Pro practice question is part of Courseiva's free Databricks certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the Databricks-ML-Pro exam.