MLS-C01 Exam Dumps - AWS Certified Machine Learning - Specialty

 A convolutional neural network (CNN) is a type of machine learning algorithm that is suitable for image classification tasks. A CNN consists of multiple layers that can extract features from images and learn to recognize patterns and objects. A CNN can also use transfer learning to leverage pre-trained models that have been trained on large-scale image datasets, such as ImageNet, and fine-tune them for specific tasks, such as detecting the company’s retail brand. A CNN can achieve high accuracy and performance for image classification problems, as it can handle complex and diverse images and reduce the dimensionality and noise of the input data. A CNN can be implemented using various frameworks and libraries, such as TensorFlow, PyTorch, Keras, MXNet, etc12

The other options are not valid or relevant for the image classification task. Latent Dirichlet Allocation (LDA) is a type of machine learning algorithm that is suitable for topic modeling tasks. LDA can discover the hidden topics and their proportions in a collection of text documents, such as news articles, tweets, reviews, etc. LDA is not applicable for image data, as it requires textual input and output. LDA can be implemented using various frameworks and libraries, such as Gensim, Scikit-learn, Mallet, etc34

Recurrent neural network (RNN) is a type of machine learning algorithm that is suitable for sequential data tasks. RNN can process and generate data that has temporal or sequential dependencies, such as natural language, speech, audio, video, etc. RNN is not optimal for image data, as it does not capture the spatial features and relationships of the pixels. RNN can be implemented using various frameworks and libraries, such as TensorFlow, PyTorch, Keras, MXNet, etc.

K-means is a type of machine learning algorithm that is suitable for clustering tasks. K-means can partition a set of data points into a predefined number of clusters, based on the similarity and distance between the data points. K-means is not suitable for image classification tasks, as it does not learn to label the images or detect the objects of interest. K-means can be implemented using various frameworks and libraries, such as Scikit-learn, TensorFlow, PyTorch, etc.

 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.

IP Insights - Amazon SageMaker

Factorization Machines Algorithm - Amazon SageMaker

Object2Vec Algorithm - Amazon SageMaker

Since false positives (identifying healthy patients as having the disease) increase costs, the key is to reduce false positives, which is captured by high precision. Precision measures how many positive predictions are actually correct.

From AWS documentation:

“Precision is the ratio of correctly predicted positive observations to the total predicted positive observations. Precision is a good measure to determine when the costs of false positives are high.”

— AWS ML Exam Guide – Evaluation Metrics

 Initializing the words by word2vec embeddings pretrained on a large collection of news articles related to the energy sector will provide the maximum performance boost for the LSTM model. Word2vec is a technique that learns distributed representations of words based on their co-occurrence in a large corpus of text. These representations capture semantic and syntactic similarities between words, which can help the LSTM model better understand the meaning and context of the sentences in the text documents. Using word2vec embeddings that are pretrained on a relevant domain (energy sector) can further improve the performance by reducing the vocabulary mismatch and increasing the coverage of the words in the text documents. References:

AWS Machine Learning Specialty Exam Guide

AWS Machine Learning Training - Text Classification with TF-IDF, LSTM, BERT: a comparison of performance

AWS Machine Learning Training - Machine Learning - Exam Preparation Path

 The best option for using Spot Instances in a long-running Amazon EMR cluster is to use them for the task nodes. Task nodes are optional nodes that are used to increase the processing power of the cluster. They do not store any data and can be added or removed without affecting the cluster’s operation. Therefore, they are more resilient to interruptions caused by Spot Instance termination. Using Spot Instances for the master node or the core nodes is not recommended, as they store important data and metadata for the cluster. If they are terminated, the cluster may fail or lose data. References:

Amazon EMR on EC2 Spot Instances

Instance purchasing options - Amazon EMR