Become Google Certified with updated Professional-Machine-Learning-Engineer exam questions and correct answers
You work for an organization that operates a streaming music service. You have a custom production model
that is serving a "next song" recommendation based on a user’s recent listening history. Your model is
deployed on a Vertex Al endpoint. You recently retrained the same model by using fresh data. The model
received positive test results offline. You now want to test the new model in production while minimizing
complexity. What should you do?
You are building a linear model with over 100 input features, all with values between -1 and 1. You suspect
that many features are non-informative. You want to remove the non-informative features from your model
while keeping the informative ones in their original form. Which technique should you use?
You work for a bank You have been asked to develop an ML model that will support loan application
decisions. You need to determine which Vertex Al services to include in the workflow You want to track the
model's training parameters and the metrics per training epoch. You plan to compare the performance of each
version of the model to determine the best model based on your chosen metrics. Which Vertex Al services
should you use?
You want to migrate a scikrt-learn classifier model to TensorFlow. You plan to train the TensorFlow classifier
model using the same training set that was used to train the scikit-learn model and then compare the
performances using a common test set. You want to use the Vertex Al Python SDK to manually log the
evaluation metrics of each model and compare them based on their F1 scores and confusion matrices. How
should you log the metrics?
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