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

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 best option for dealing with the missing categorical variable in the test set is to apply one-hot encoding on the categorical variables in the test data. This option has the following advantages:

It ensures the consistency and compatibility of the data format for the ML model, as the one-hot encoding transforms the categorical variables into binary vectors that can be easily processed by the model. By applying one-hot encoding on the categorical variables in the test data, you can match the number and order of the features in the test data with the training data, and avoid any errors or discrepancies in the model prediction.

It preserves the information and relevance of the data for the ML model, as the one-hot encoding creates a separate feature for each possible value of the categorical variable, and assigns a value of 1 to the feature corresponding to the actual value of the variable, and 0 to the rest. By applying one-hot encoding on the categorical variables in the test data, you can retain the original meaning and importance of the categorical variable, and avoid any loss or distortion of the data.

The other options are less optimal for the following reasons:

Option A: Randomly redistributing the data, with 70% for the training set and 30% for the test set, introduces additional complexity and risk. This option requires reshuffling and splitting the data again, which can be tedious and time-consuming. Moreover, this option may not guarantee that the missing categorical variable will be present in the test set, as it depends on the randomness of the data distribution. Furthermore, this option may affect the quality and validity of the ML model, as it may change the data characteristics and patterns that the model has learned from the original training set.

Option B: Using sparse representation in the test set introduces additional overhead and inefficiency. This option requires converting the categorical variables in the test set into sparse vectors, which are vectors that have mostly zero values and only store the indices and values of the non-zero elements. However, using sparse representation in the test set may not be compatible with the ML model, as the model expects the input data to have the same format and dimensionality as the training data, which uses one-hot encoding. Moreover, using sparse representation in the test set may not be efficient or scalable, as it requires additional computation and memory to store and process the sparse vectors.

Option D: Collecting more data representing all categories introduces additional cost and delay. This option requires obtaining and labeling more data that contains the missing categorical variable, which can be expensive and time-consuming. Moreover, this option may not be feasible or necessary, as the missing categorical variable may not be available or relevant for the test data, depending on the data source or the business problem.

The best option for developing a solution that predicts the presence and severity of the disease with high accuracy is to develop an image segmentation ML model to locate the boundaries of the rust spots. Image segmentation is a technique that partitions an image into multiple regions, each corresponding to a different object or semantic category. Image segmentation can be used to detect and localize the rust spots in the images of crops, and measure their shape and size. This information can then be used to determine the presence and severity of the disease, as the rust spots are correlated to the disease symptoms. Image segmentation can also handle the variability of the rust spots, as it does not rely on predefined templates or thresholds. Image segmentation can be implemented using deep learning models, such as U-Net, Mask R-CNN, or DeepLab, which can learn from large-scale datasets and achieve high accuracy and robustness. The other options are not as suitable for developing a solution that predicts the presence and severity of the disease with high accuracy, because:

Creating an object detection model that can localize the rust spots would only provide the bounding boxes of the rust spots, not their exact boundaries. This would result in less precise measurements of the shape and size of the rust spots, and might affect the accuracy of the disease prediction. Object detection models are also more complex and computationally expensive than image segmentation models, as they have to perform both classification and localization tasks.

Developing a template matching algorithm using traditional computer vision libraries would require manually designing and selecting the templates for the rust spots, which might not capture the diversity and variability of the rust spots. Template matching algorithms are also sensitive to noise, occlusion, rotation, and scale changes, and might fail to detect the rust spots in different scenarios. Template matching algorithms are also less accurate and robust than deep learning models, as they do not learn from data.

Developing an image classification ML model to predict the presence of the disease would only provide a binary or categorical output, not the location or severity of the disease. Image classification models are also less informative and interpretable than image segmentation models, as they do not provide any spatial information or visual explanation for the prediction. Image classification models might also suffer from class imbalance or mislabeling issues, as the presence of the disease might not be consistent or clear across the images.  References :

Image Segmentation | Computer Vision | Google Developers

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