Free Databricks Machine Learning Associate Actual Exam Questions - Question 6 Discussion
Question No. 6
A data scientist is attempting to tune a logistic regression model logistic using scikit-learn. They want
to specify a search space for two hyperparameters and let the tuning process randomly select values
for each evaluation.
They attempt to run the following code block, but it does not accomplish the desired task:
Which of the following changes can the data scientist make to accomplish the task?
to specify a search space for two hyperparameters and let the tuning process randomly select values
for each evaluation.
They attempt to run the following code block, but it does not accomplish the desired task:
Which of the following changes can the data scientist make to accomplish the task?
Select one option, then reveal solution.
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Shah C.
2026-02-12
It’s A because RandomizedSearchCV handles random sampling directly, unlike GridSearchCV.
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Carlos N.
2026-01-30
It’s A because RandomizedSearchCV is designed for random sampling of hyperparameters, unlike GridSearchCV which does exhaustive search. No need to mess with random_state or parameter formats here.
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CN
Carlos N.
2026-01-22
A imo, switching to RandomizedSearchCV is key since GridSearchCV exhaustively checks all combos. The other options either don't handle randomness or mess with parameter formatting.
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CN
Carlos N.
2026-01-17
A. GridSearchCV tries all combos, not random picks. RandomizedSearchCV fits the random selection part way better here. Other options seem off for this task.
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