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PROFESSIONAL-MACHINE-LEARNING-ENGINEER · Question #11
You have a functioning end-to-end ML pipeline that involves tuning the hyperparameters of your ML model using AI Platform, and then using the best-tuned parameters for training. Hypertuning is taking
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Submitted by akirajp· Apr 18, 2026ML model development
Question
You have a functioning end-to-end ML pipeline that involves tuning the hyperparameters of your ML model using AI Platform, and then using the best-tuned parameters for training. Hypertuning is taking longer than expected and is delaying the downstream processes. You want to speed up the tuning job without significantly compromising its effectiveness. Which actions should you take? (Choose two.)
Options
- ADecrease the number of parallel trials.
- BDecrease the range of floating-point values.
- CSet the early stopping parameter to TRUE.
- DChange the search algorithm from Bayesian search to random search.
- EDecrease the maximum number of trials during subsequent training phases.
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Topics
#Hyperparameter Tuning#Model Optimization#Early Stopping#Tuning Efficiency