Google Cloud Certified Professional Machine Learning Engineer Practice Mode

Community Discussion

Written by other learners. Comments can disagree with the marked answer — check the official documentation when in doubt.

• Chose CE2

  apart from early stopping which no one has doubt about E (reduce max trails has the lowest propensity to reduce performance)

• Chose BC1

  Decreasing the range of floating-point values reduces the search space for the hyperparameter tuning process. A smaller search space allows the algorithm to converge faster to an optimal solution by focusing only on a narrower range of values. This approach speeds up tuning without significantly compromising effectiveness, as the range is constrained to more reasonable values. Why C is correct: Setting the early stopping parameter to TRUE enables the tuning process to stop trials early if it becomes clear that a given trial is not improving or yielding promising results. This prevents unnecessary computation and saves time by discarding underperforming configurations early in the process.

• Chose CE1

  C & E are the choices