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Exam Professional Machine Learning Engineer All Questions

View all questions & answers for the Professional Machine Learning Engineer exam

Exam Professional Machine Learning Engineer topic 1 question 290 discussion

Actual exam question from Google's Professional Machine Learning Engineer
Question #: 290
Topic #: 1
[All Professional Machine Learning Engineer Questions]

You developed a Python module by using Keras to train a regression model. You developed two model architectures, linear regression and deep neural network (DNN), within the same module. You are using the training_method argument to select one of the two methods, and you are using the learning_rate and num_hidden_layers arguments in the DNN. You plan to use Vertex AI's hypertuning service with a budget to perform 100 trials. You want to identify the model architecture and hyperparameter values that minimize training loss and maximize model performance. What should you do?

  • A. Run one hypertuning job for 100 trials. Set num_hidden_layers as a conditional hyperparameter based on its parent hyperparameter training_method, and set learning_rate as a non-conditional hyperparameter.
  • B. Run two separate hypertuning jobs, a linear regression job for 50 trials, and a DNN job for 50 trials. Compare their final performance on a common validation set, and select the set of hyperparameters with the least training loss.
  • C. Run one hypertuning job with training_method as the hyperparameter for 50 trials. Select the architecture with the lowest training loss, and further hypertune it and its corresponding hyperparameters tor 50 trials.
  • D. Run one hypertuning job for 100 trials. Set num_hidden_layers and learning_rate as conditional hyperparameters based on their parent hyperparameter training_method.
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Suggested Answer: A 🗳️

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carolctech
4 weeks ago
Selected Answer: A
The best approach is A and here's why: The use of the 100 trials in a single job by using conditional hyperparameters maximizes budget efficiency. The number of hidden layers should be conditional, because it is relevant only for NON-LINEAR models like neural networks (which is DNN's case) and not for linear models -- where hidden layers don't exist. Learning rate is relevant for both models, unless the question stated that the regression model used a closed-form solution, not a gradient-based optimization method.
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JDpmle2024
4 weeks, 1 day ago
Selected Answer: D
This would allow you to first set the type of job, and only after that any other parameters. So first, select training_method. If training_method is DNN, then you specify the other parameters.
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