Databricks-ML-Assoc Model Development Practice Question
A machine learning engineer is tuning a scikit-learn GradientBoostingClassifier on Databricks. They want to run 40 hyperparameter combinations, each trained on the full dataset, while keeping the driver free of model training work and collecting all results in a single MLflow parent run. Which approach should they use?
⚠ Common exam trap
The trap here is assuming that any parallel search option, such as n_jobs=-1, distributes work across the cluster when it actually only uses the driver's local cores.
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 Hyperopt with the default SparkTrials and set max_evals to 40.
Distributing many single-node model fits requires a mechanism that schedules each trial as cluster work rather than driver-local computation. SparkTrials in Hyperopt does exactly this, and with MLflow autologging each trial becomes a nested run beneath one parent run, satisfying both the parallelism and the consolidated tracking requirements in a single, supported pattern.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Use scikit-learn's GridSearchCV with n_jobs=-1 inside a single notebook cell.
Why it's wrong here
GridSearchCV with n_jobs=-1 parallelizes across local CPU cores of the driver, not across the Databricks cluster's worker nodes. On a multi-node cluster this leaves worker resources idle and can exhaust driver memory when 40 fits run concurrently. It also produces no integrated MLflow parent run structure on its own, so results are harder to compare.
- ✗
Use Hyperopt with Trials and wrap the objective function in a Pandas UDF.
Why it's wrong here
The serial Trials class runs every evaluation sequentially inside the driver process, so the driver performs all 40 model fits and becomes the bottleneck. Wrapping the objective in a Pandas UDF does not help because Hyperopt's serial Trials does not execute trials as Spark tasks. This combination therefore contradicts the requirement to keep training work off the driver.
- ✗
Launch 40 separate notebooks with dbutils.notebook.run and merge their runs afterward.
Why it's wrong here
dbutils.notebook.run invokes child notebooks, but each child starts its own MLflow run rather than a nested run under one parent unless extra run-management code is added. This creates 40 unrelated experiments and heavy orchestration overhead. It also does not parallelize the training itself; the child notebooks still execute their fits wherever they are scheduled, without SparkTrials' trial distribution.
- ✓
Use Hyperopt with the default SparkTrials and set max_evals to 40.
Why this is correct
SparkTrials distributes each hyperparameter trial as a Spark job across worker nodes, so the driver is not consumed by model fitting. With MLflow autologging enabled, SparkTrials records each trial as a nested run under a single parent run, giving one consolidated view of all 40 evaluations. This is the native Databricks pattern for parallel single-node algorithm tuning at this scale.
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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
This Databricks-ML-Assoc 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-Assoc exam.