MLS-C01 Leak Questions

A pronunciation lexicon is a file that defines how words or phrases should be pronounced by Amazon Polly. A lexicon can help customize the speech output for words that are uncommon, foreign, or have multiple pronunciations. A lexicon must conform to the Pronunciation Lexicon Specification (PLS) standard and can be stored in an AWS region using the Amazon Polly API. To use a lexicon for synthesizing speech, the lexicon name must be specified in the SSML tag. For example, the following lexicon defines how to pronounce the acronym W3C:

W3C World Wide Web Consortium

To use this lexicon, the text input must include the following SSML tag:

xmlns=“http://www.w3.org/2001/10/synthesis” xml:lang=“en-US” > The W3C is an international community that develops open standards to ensure the long-term growth of the Web.

References:

• Customize pronunciation using lexicons in Amazon Polly: A blog post that explains how to use lexicons for creating custom pronunciations.
• Managing Lexicons: A documentation page that describes how to store and retrieve lexicons using the Amazon Polly API.

• 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 MAPE of the predictor could be improved by making the following changes to the CreatePredictor API call:

• Set PerformAutoML to true. This will allow Amazon Forecast to automatically evaluate different algorithms and choose the one that minimizes the objective function, which is the mean of the weighted losses over the forecast types. By default, these are the p10, p50, and p90 quantile losses1. This option can help find a better algorithm than DeepAR+ for the given data.
• Set PerformHPO to true. This will enable hyperparameter optimization (HPO), which is the process of finding the optimal values for the algorithm-specific parameters that affect the quality of the forecasts. HPO can improve the accuracy of the predictor by tuning the hyperparameters based on the training data2.

The other options are not likely to improve the MAPE of the predictor. Setting ForecastHorizon to 4 will reduce the number of time steps that the model predicts, which may not match the business requirement of predicting next month’s inventory. Setting ForecastFrequency to W for weekly will change the granularity of the forecasts, which may not be appropriate for the monthly data. Setting FeaturizationMethodName to filling will not have any effect, since there is no missing data in the dataset.

References:

• CreatePredictor - Amazon Forecast
• HPOConfig - Amazon Forecast

 XGBoost is a built-in Amazon SageMaker machine learning algorithm that should be used for modeling the credit card fraud detection problem. XGBoost is an algorithm that implements a scalable and distributed gradient boosting framework, which is a popular and effective technique for supervised learning problems. Gradient boosting is a method of combining multiple weak learners, such as decision trees, into a strong learner, by iteratively fitting new models to the residual errors of the previous models and adding them to the ensemble. XGBoost can handle various types of data, such as numerical, categorical, or text, and can perform both regression and classification tasks. XGBoost also supports various features and optimizations, such as regularization, missing value handling, parallelization, and cross-validation, that can improve the performance and efficiency of the algorithm.

XGBoost is suitable for the credit card fraud detection problem for the following reasons:

• The problem is a binary classification problem, where the goal is to predict whether a transaction is fraudulent or not, based on the information from new transactions. XGBoost can perform binary classification by using a logistic regression objective function and outputting the probability of the positive class (fraudulent) for each transaction.
• The problem involves a large and imbalanced dataset of historical data labeled as fraudulent. XGBoost can handle large-scale and imbalanced data by using distributed and parallel computing, as well as techniques such as weighted sampling, class weighting, or stratified sampling, to balance the classes and reduce the bias towards the majority class (non-fraudulent).
• The problem requires a high accuracy and precision for detecting fraudulent transactions, as well as a low false positive rate for avoiding false alarms. XGBoost can achieve high accuracy and precision by using gradient boosting, which can learn complex and non-linear patterns from the data and reduce the variance and overfitting of the model. XGBoost can also achieve a low false positive rate by using regularization, which can reduce the complexity and noise of the model and prevent it from fitting spurious signals in the data.

The other options are not as suitable as XGBoost for the credit card fraud detection problem for the following reasons:

• Seq2seq: Seq2seq is an algorithm that implements a sequence-to-sequence model, which is a type of neural network model that can map an input sequence to an output sequence. Seq2seq is mainly used for natural language processing tasks, such as machine translation, text summarization, or dialogue generation. Seq2seq is not suitable for the credit card fraud detection problem, because the problem is not a sequence-to-sequence task, but a binary classification task. The input and output of the problem are not sequences of words or tokens, but vectors of features and labels.
• K-means: K-means is an algorithm that implements a clustering technique, which is a type of unsupervised learning method that can group similar data points into clusters. K-means is mainly used for exploratory data analysis, dimensionality reduction, or anomaly detection. K-means is not suitable for the credit card fraud detection problem, because the problem is not a clustering task, but a classification task. The problem requires using the labeled data to train a model that can predict the labels of new data, not finding the optimal number of clusters or the cluster memberships of the data.
• Random Cut Forest (RCF): RCF is an algorithm that implements an anomaly detection technique, which is a type of unsupervised learning method that can identify data points that deviate from the normal behavior or distribution of the data. RCF is mainly used for detecting outliers, frauds, or faults in the data. RCF is not suitable for the credit card fraud detection problem, because the problem is not an anomaly detection task, but a classification task. The problem requires using the labeled data to train a model that can predict the labels of new data, not finding the anomaly scores or the anomalous data points in the data.

References:

• XGBoost Algorithm
• Use XGBoost for Binary Classification with Amazon SageMaker
• Seq2seq Algorithm
• K-means Algorithm
• [Random Cut Forest Algorithm]