Hyperparameter Tuning MACHINE-LEARNING-ASSOCIATE Practice Questions (Page 4)

Questions 16–20

1. 16foundation · easy

   What is the recommended way to handle a trial that fails during a Hyperopt tuning run?

   • ALet the entire tuning run fail and start over
   • BIgnore the failure and continue with the next trial automatically
   • CIncrease the parallelism to reduce the chance of failure
   • DCatch the exception in the objective function and return a large loss value
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2. 17application · medium

   A team is comparing hyperparameter tuning methods for a model where each evaluation takes 10 minutes. They have a budget of 50 evaluations and want to maximize the chance of finding good hyperparameters. They decide to use Bayesian optimization. What is the key advantage of this approach over random search?

   • ABayesian optimization always finds the global optimum within the first few evaluations, regardless of the search space.
   • BBayesian optimization builds a probabilistic model of the objective function to select promising hyperparameters, focusing evaluations where improvement is likely.
   • CBayesian optimization evaluates all hyperparameter combinations in a predefined grid, ensuring the global optimum is found.
   • DBayesian optimization samples hyperparameters uniformly from the search space, providing unbiased estimates of performance.
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4. 18foundation · easy

   Which of the following is NOT a core argument of the Hyperopt `fmin` function?

   • Amax_evals
   • Bloss
   • Cspace
   • Dfn
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5. 19foundation · easy

   What is the defining characteristic of grid search for hyperparameter tuning?

   • AIt uses a probabilistic model to guide the search
   • BIt starts with a random point and iteratively improves it
   • CIt samples hyperparameters randomly from a distribution
   • DIt exhaustively evaluates all combinations of a predefined set of hyperparameter values
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6. 20foundation · easy

   What is the core idea behind Bayesian optimization for hyperparameter tuning?

   • AIt exhaustively evaluates all combinations of hyperparameters
   • BIt uses gradient descent to find the minimum of the objective function
   • CIt randomly samples hyperparameters from a uniform distribution
   • DIt builds a probabilistic model of the objective function to select promising hyperparameters
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