Databricks-ML-Assoc Databricks Machine Learning Practice Question
A machine learning engineer is training a model using Databricks AutoML. They notice that the generated notebook includes a step that uses Hyperopt for hyperparameter tuning, but the tuning process is taking too long. They want to reduce the search space without sacrificing model performance significantly. Which Hyperopt configuration change should they make?
⚠ Common exam trap
The trap here is thinking that changing the optimization algorithm alone will always speed up tuning, but without reducing the search space, the algorithm may still explore many configurations.
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 narrower search space by specifying more constrained ranges for hyperparameters, such as reducing the maximum depth of trees or limiting the number of leaves.
Narrowing the hyperparameter search space by constraining ranges reduces the number of possible configurations Hyperopt must evaluate. This directly cuts tuning time and often maintains model performance because it focuses on sensible values. Switching algorithms or increasing trials does not address the root cause of a large search space, and may even increase runtime.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Switch from the default 'TPE' algorithm to 'Random' search.
Why it's wrong here
Random search can be faster per iteration but often requires more iterations to find a good configuration. It does not inherently reduce the search space; it just changes the sampling strategy. For a large search space, random search may still explore many irrelevant regions, so it is not the most effective way to reduce tuning time while preserving performance.
- ✓
Use a narrower search space by specifying more constrained ranges for hyperparameters, such as reducing the maximum depth of trees or limiting the number of leaves.
Why this is correct
Constraining hyperparameter ranges directly reduces the search space, allowing Hyperopt to focus on promising regions. This can significantly cut tuning time while often maintaining or even improving performance because it avoids extreme values that may lead to overfitting or long training times. It is a targeted approach to balance speed and accuracy.
- ✗
Change the search algorithm to 'Annealing' and set a high initial temperature.
Why it's wrong here
Annealing is an optimization algorithm that can escape local minima, but a high initial temperature encourages more exploration, which may increase the number of trials needed to converge. It does not directly reduce the search space; it alters the exploration strategy. This could lead to longer tuning times, not shorter, and may not preserve performance efficiently.
- ✗
Increase the max_evals parameter to allow more trials.
Why it's wrong here
Increasing max_evals would increase the number of trials, making the tuning process longer, not shorter. The goal is to reduce tuning time, so this change is counterproductive. While more trials might find a better model, it does not address the need to speed up the process, and it may waste resources on unpromising configurations.
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Last reviewed September 2026 · checked against the official Databricks exam blueprint
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