Become Google Certified with updated Professional-Machine-Learning-Engineer exam questions and correct answers
You are training an object detection machine learning model on a dataset that consists of three million X-ray images, each roughly 2 GB in size. You are using Vertex AI Training to run a custom training application on a Compute Engine instance with 32-cores, 128 GB of RAM, and 1 NVIDIA P100 GPU. You notice that model training is taking a very long time. You want to decrease training time without sacrificing model performance. What should you do?
You built a deep learning-based image classification model by using on-premises data. You want to use Vertex
Al to deploy the model to production Due to security concerns you cannot move your data to the cloud. You
are aware that the input data distribution might change over time You need to detect model performance
changes in production. What should you do?
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?
You work on a team that builds state-of-the-art deep learning models by using the TensorFlow framework. Your team runs multiple ML experiments each week which makes it difficult to track the experiment runs.
You want a simple approach to effectively track, visualize and debug ML experiment runs on Google Cloud
while minimizing any overhead code. How should you proceed?
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