Databricks-ML-Pro Model Development Practice Question
A machine learning engineer is using Hyperopt with SparkTrials on a Databricks cluster to tune a gradient boosting model. They notice that the tuning job is running slowly because each trial trains on the full dataset, and they want to speed up the search without sacrificing final model quality. Which approach is most appropriate?
⚠ Common exam trap
The trap here is focusing on parallelization or trial count when the bottleneck is per-trial training time due to full dataset usage.
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 a smaller subset of the training data for each trial and then retrain the best model on the full dataset.
Using a data subset for hyperparameter tuning accelerates the search by reducing training time per trial, enabling more configurations to be evaluated. Once the best hyperparameters are found, retraining on the full dataset ensures the final model benefits from all available data. This balances efficiency and quality, especially when full-dataset training is expensive.
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 a smaller subset of the training data for each trial and then retrain the best model on the full dataset.
Why this is correct
Training on a subset for hyperparameter search reduces the time per trial, allowing more configurations to be explored quickly. After identifying the best hyperparameters, retraining on the full dataset recovers full model quality. This is a common and effective strategy when the dataset is large and individual trials are slow, as it balances search efficiency with final performance.
- ✗
Increase the parallelism parameter in SparkTrials to run more trials concurrently.
Why it's wrong here
Increasing parallelism can speed up the overall search by running more trials at once, but it does not reduce the time each trial takes. If each trial trains on the full dataset, the cluster may become resource-constrained, leading to slower individual trials or failures. While it can help, it is not the most direct way to address the slow per-trial training time.
- ✗
Reduce the number of trials and increase the max_evals parameter to compensate.
Why it's wrong here
Reducing the number of trials while increasing max_evals is contradictory because max_evals defines the total number of trials. This would not speed up individual trials and could actually increase total runtime. The goal is to make each trial faster, not to change the total count in a way that confuses the search. This approach does not address the per-trial training time.
- ✗
Switch from SparkTrials to Trials to avoid distributed overhead.
Why it's wrong here
Switching to Trials would run trials sequentially on the driver, which is likely slower for large datasets because it loses parallelism. SparkTrials distributes trials across worker nodes, which can speed up the overall search. The overhead of Spark is usually outweighed by parallel execution. This change would not address the per-trial training time and could worsen overall runtime.
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Last reviewed September 2026 · checked against the official Databricks exam blueprint
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