Practice Test 3 | Google Cloud Certified Professional Data Engineer | Dumps | Mock Test
You are building a machine learning model using TensorFlow. The model aims to predict the next earthquake’s locations, approximate time and Richter scale based on data records since 1913. The model needs tuning each number of epochs on training data for higher accuracy. Which of the variables are used for hyperparameter tuning? (Choose 2 options)
A. Number of features
B. Number of hidden layers
C. Number of nodes in hidden layers
D. Weight values
Correct Answer: B and C
Hyperparameters are the variables that govern the training process itself. For example, part of setting up a deep neural network is deciding how many hidden layers of nodes to use between the input layer and the output layer, and how many nodes each layer should use. These variables are not directly related to the training data but these are configuration variables. Note that parameters change during a training job, while hyperparameters are usually constant during a job.
Option A is incorrect: Numbers of features is set by feature engineering, not hyperparameter tuning.
Option D is incorrect: Weight values are set while training the model.
Source(s):
Hyperparameter Tuning:
https://cloud.google.com/ml-engine/docs/tensorflow/hyperparameter-tuning-overview
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