Google Professional-Machine-Learning-Engineer 問題集 : Google Professional Machine Learning Engineer

You work for a hospital. You received approval to collect the necessary patient data, and you trained a Vertex AI tabular AutoML model that calculates patients' risk score for hospital admission. You deployed the model. However, you're concerned that patient demographics might change over time and alter the feature interactions and impact prediction accuracy. You want to be alerted if feature interactions change, and you want to understand the importance of the features for the predictions. You want your alerting approach to minimize cost. What should you do?

• A. Create a feature drift monitoring job. Set the sampling rate to 1 and the monitoring frequency to weekly.
• B. Create a feature drift monitoring job. Set the sampling rate to 0.1 and the monitoring frequency to weekly.
• C. Create a feature attribution drift monitoring job. Set the sampling rate to 1 and the monitoring frequency to weekly.
• D. Create a feature attribution drift monitoring job. Set the sampling rate to 0.1 and the monitoring frequency to weekly.

Your company manages an application that aggregates news articles from many different online sources and sends them to users. You need to build a recommendation model that will suggest articles to readers that are similar to the articles they are currently reading. Which approach should you use?

• A. Manually label a few hundred articles, and then train an SVM classifier based on the manually classified articles that categorizes additional articles into their respective categories.
• B. Encode all articles into vectors using word2vec, and build a model that returns articles based on vector similarity.
• C. Create a collaborative filtering system that recommends articles to a user based on the user's past behavior.
• D. Build a logistic regression model for each user that predicts whether an article should be recommended to a user.

A regulator requires your bank to provide, for each declined loan application, the specific applicant attributes that most influenced the decision. Your model is a deep neural network deployed to an online endpoint. You need to satisfy this requirement without replacing the model.
What should you do?

• A. Publish the model's global feature importance scores computed during training.
• B. Configure the deployed model with Vertex Explainable AI using integrated gradients, and return per-prediction feature attributions with each response.
• C. Log all input features to BigQuery, and let analysts inspect the raw values for declined applications.
• D. Train a separate logistic regression model on the same features, and use its coefficients to explain the neural network's decisions.

You are developing a model to identify traffic signs in images extracted from videos taken from the dashboard of a vehicle. You have a dataset of 100,000 images that were cropped to show one out of ten different traffic signs. The images have been labeled accordingly for model training, and are stored in a Cloud Storage bucket. You need to be able to tune the model during each training run. How should you train the model?

• A. Train a model for image classification by using Vertex AI AutoML.
• B. Train a model for object detection by using Vertex AI AutoML.
• C. Develop the model training code for image classification, and train a model by using Vertex AI custom training.
• D. Develop the model training code for object detection, and train a model by using Vertex AI custom training.

You are an ML engineer at a large grocery retailer with stores in multiple regions. You have been asked to create an inventory prediction model. Your model's features include region, location, historical demand, and seasonal popularity. You want the algorithm to learn from new inventory data on a daily basis. Which algorithms should you use to build the model?

• A. Classification
• B. Recurrent Neural Networks (RNN)
• C. Reinforcement Learning
• D. Convolutional Neural Networks (CNN)