Google Professional-Machine-Learning-Engineer Exam With Confidence Using Practice Dumps

To develop a credit risk model that meets the regulatory requirements and can be monitored over time, you should follow these steps:

Use Vertex Explainable AI with the sampled Shapley method.  Vertex Explainable AI is a service that provides feature attributions for machine learning models, which can help you understand how each feature contributes to the prediction 1 .  The sampled Shapley method is a technique that estimates the Shapley values for each feature, which are based on the marginal contribution of each feature to the prediction across all possible feature subsets 2 .  The sampled Shapley method is suitable for neural networks and other complex models, as it can capture the non-linear and interaction effects of the features 3 .

Enable Vertex AI Model Monitoring to check for feature distribution drift.  Vertex AI Model Monitoring is a service that helps you track and manage the performance and quality of your deployed models over time 4 . Feature distribution drift is a type of data drift that occurs when the distribution of the input features changes significantly from the training data, which can affect the model accuracy and reliability. By checking for feature distribution drift, you can detect when your model needs to be retrained or updated with new data.

Option A is incorrect because creating a collaborative filtering system that recommends articles to a user based on the user’s past behavior is not the best approach to suggest articles that are similar to the articles they are currently reading.  Collaborative filtering is a method of recommendation that uses the ratings or prefe rences of other users to predict the preferences of a target user 1 . However, this method does not consider the content or features of the articles, and may not be able to find articles that are similar in terms of topic, style, or sentiment.

Option B is correct because encoding all articles into vectors using word2vec, and building a model that returns articles based on vector similarity is a suitable approach to suggest articles that are similar to the articles they are currently reading.  Word2vec is a technique that learns low-dimensional and dense representations of words from a large corpus o f text, such that words that are semantically similar have similar vectors 2 . By applying word2vec to the articles, we can obtain vector representations of the articles that capture their meaning and usage.  Then, we can use a similarity measure, such as cosine similarity, to find articles that have similar vectors to the current article 3 .

Option C is incorrect because building a logistic regression model for each user that predicts whether an article should be recommended to a user is not a feasible approach to suggest articles that are similar to the articles they are currently reading.  Logistic regression is a supervised learning method that models the probability of a binary outcome (such as recommend or not) based on some input features (such as user prof ile or article content) 4 . However, this method requires a large amount of labeled data for each user, which may not be available or scalable. Moreover, this method does not directly measure the similarity between articles, but rather the likelihood of a user’s preference.

Option D is incorrect because manually labeling a few hundred articles, and then training an SVM classifier based on the manually classified articles that categorizes additional articles into their respective categories is not an effective approach to suggest articles that are similar to the articles they are currently reading.  SVM (support vector machine) is a supervised learning method that finds a hyperplane that separates the data into different classes (such as news categories) with the maximum margin 5 . However, this method also requires a large amount of labeled data, which may be costly and time-consuming to obtain. Moreover, this method does not account for the fine-grained similarity between articles within the same category, or the cross-category similarity between articles from different categories.

The most likely reason for the observed result is that the model is overfitting in areas with less traffic and underfitting in areas with more traffic. Overfitting means that the model learns the specific patterns and noise in the training data, but fails to generalize well to new and unseen data. Underfitting means that the model is not able to capture the complexity and variability of the data, and performs poorly on both training and test data. In this case, the model might have learned to segment the images well when there is less traffic, but it might not have enough data or features to handle the more challenging scenarios when there is more traffic. This could lead to a decrease in the AUC metric, which measures the ability of the model to distinguish between different classes. AUC is a suitable metric for this classification model, as it is not affected by class imbalance or threshold selection. The other options are not likely to be the reason for the result, as they are not related to the traffic density. Too much data representing congested areas would not cause the model to fail in those areas, but rather help the model learn better. Gradients vanishing or exploding is a problem that occurs during the training process, not after the deployment, and it affects the whole model, not specific scenarios.  References :

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