Authentic Amazon Web Services Certification Exam MLS-C01 Questions Answers

Option C is correct because augmenting the images in the dataset can help the model learn more features and generalize better to new products. Image augmentation is a common technique to increase the diversity and size of the training data.

Option E is correct because Amazon Rekognition Custom Labels can train a custom model to detect specific objects and scenes that are relevant to the business use case. It can also leverage the existing models from Amazon Rekognition that are trained on tens of millions of images across many categories.

Option F is correct because class imbalance can affect the performance and accuracy of the model, as it can cause the model to be biased towards the majority class and ignore the minority class. Applying oversampling or undersampling can help balance the classes and improve the model’s ability to learn from the data.

Option A is incorrect because the semantic segmentation algorithm is used to assign a label to every pixel in an image, not to classify the whole image into a category. Semantic segmentation is useful for applications such as autonomous driving, medical imaging, and satellite imagery analysis.

Option B is incorrect because the DetectLabels API is a general-purpose image analysis service that can detect objects, scenes, and concepts in an image, but it cannot be customized to the specific product lines of the ecommerce company. The DetectLabels API is based on the pre-trained models from Amazon Rekognition, which may not cover all the categories that the company needs.

Option D is incorrect because normalizing the pixels and scaling the images are preprocessing steps that should be done before training the model, not after. These steps can help improve the model’s convergence and performance, but they are not sufficient to increase the accuracy of the model on new products.

References:

: Image Augmentation - Amazon SageMaker

: Amazon Rekognition Custom Labels Features

: [Handling Imbalanced Datasets in Machine Learning]

: [Semantic Segmentation - Amazon SageMaker]

: [DetectLabels - Amazon Rekognition]

: [Image Classification - MXNet - Amazon SageMaker]

: [https://towardsdatascience.com/handling-imbalanced-datasets-in-machine-learning-7a0e84220f28]

: [https://docs.aws.amazon.com/sagemaker/latest/dg/semantic-segmentation.html]

: [https://docs.aws.amazon.com/rekognition/latest/dg/API_DetectLabels.html]

: [https://docs.aws.amazon.com/sagemaker/latest/dg/image-classification.html]

: [https://towardsdatascience.com/handling-imbalanced-datasets-in-machine-learning-7a0e84220f28]

: [https://docs.aws.amazon.com/sagemaker/latest/dg/semantic-segmentation.html]

: [https://docs.aws.amazon.com/rekognition/latest/dg/API_DetectLabels.html]

: [https://docs.aws.amazon.com/sagemaker/latest/dg/image-classification.html]

: [https://towardsdatascience.com/handling-imbalanced-datasets-in-machine-learning-7a0e84220f28]

: [https://docs.aws.amazon.com/sagemaker/latest/dg/semantic-segmentation.html]

: [https://docs.aws.amazon.com/rekognition/latest/dg/API_DetectLabels.html]

: [https://docs.aws.amazon.com/sagemaker/latest/dg/image-classification.html]

 The IP Insights algorithm is designed to capture associations between entities and IP addresses, and can be used to identify anomalous IP usage patterns. The algorithm can learn from historical data that contains pairs of entities and IP addresses, and can return a score that indicates how likely the pair is to occur. The company can use this algorithm to train a model that can detect when a customer is accessing the site from a different location than usual, and request additional information accordingly. The company can also schedule updates and retraining of the model using new log data nightly to keep the model up to date with the latest IP usage patterns.

The other options are not suitable for this use case because:

Option A: The factorization machines (FM) algorithm is a general-purpose supervised learning algorithm that can be used for both classification and regression tasks. However, it is not optimized for capturing associations between entities and IP addresses, and would require labeling each record as either a successful or failed access attempt, which is a costly and time-consuming process.

Option C: The IP Insights algorithm is a good choice for this use case, but it does not require labeling each record as either a successful or failed access attempt. The algorithm is unsupervised and can learn from the historical data without labels. Labeling the data would be unnecessary and wasteful.

Option D: The Object2Vec algorithm is a general-purpose neural embedding algorithm that can learn low-dimensional dense embeddings of high-dimensional objects. However, it is not designed to capture associations between entities and IP addresses, and would require a different input format than the one provided by the company. The Object2Vec algorithm expects pairs of objects and their relationship labels or scores as inputs, while the company has data containing the source IP addresses and the login names of the requesting users.

References:

IP Insights - Amazon SageMaker

Factorization Machines Algorithm - Amazon SageMaker

Object2Vec Algorithm - Amazon SageMaker

One-hot encoding is a technique that can be used to convert a categorical variable, such as the Day-Of_Week column, to binary values. One-hot encoding creates a new binary column for each unique value in the original column, and assigns a value of 1 to the column that corresponds to the value in the original column, and 0 to the rest. For example, if the original column has values Monday, Tuesday, Wednesday, Thursday, Friday, Saturday, and Sunday, one-hot encoding will create seven new columns, each representing one day of the week. If the value in the original column is Tuesday, then the column for Tuesday will have a value of 1, and the other columns will have a value of 0. One-hot encoding can help improve the performance of machine learning models, as it eliminates the ordinal relationship between the values and creates a more informative and sparse representation of the data.

References:

One-Hot Encoding - Amazon SageMaker

One-Hot Encoding: A Simple Guide for Beginners | by Jana Schmidt …

One-Hot Encoding in Machine Learning | by Nishant Malik | Towards …