Amazon Web Services MLS-C01 Exam With Confidence Using Practice Dumps

 The business problem of predicting whether a new credit card applicant will default on a credit card payment can be framed as a binary classification problem. Binary classification is the task of predicting a discrete class label output for an example, where the class label can only take one of two possible values. In this case, the class label can be either “default” or “no default”, indicating whether the applicant will or will not default on a credit card payment. A binary classification model can return the probability that a given applicant belongs to each class, and then assign the applicant to the class with the highest probability. For example, if the model predicts that an applicant has a 0.8 probability of defaulting and a 0.2 probability of not defaulting, then the model will classify the applicant as “default”. Binary classification is suitable for this problem because the outcome of interest is categorical and binary, and the model needs to return the probability of each outcome.

AWS Machine Learning Specialty Exam Guide

AWS Machine Learning Training - Classification vs Regression in Machine Learning

The Recordio protobuf format is a binary data format that is optimized for training on SageMaker. It allows faster data loading and lower memory usage compared to other formats such as CSV or numpy arrays. The Recordio protobuf format also supports features such as sparse input, variable-length input, and label embedding. To use the Recordio protobuf format, the data needs to be serialized and deserialized using the appropriate libraries. Some of the built-in algorithms in SageMaker support the Recordio protobuf format as a content type for training and inference. References:

Common Data Formats for Training

Using RecordIO Format

Content Types Supported by Built-in Algorithms

To capture word context and sequential QA information, the embedding vectors need to consider both the order and the meaning of the words in the text.

Option B, Amazon SageMaker BlazingText algorithm in Skip-gram mode, is a valid option because it can learn word embeddings that capture the semantic similarity and syntactic relations between words based on their co-occurrence in a window of words. Skip-gram mode can also handle rare words better than continuous bag-of-words (CBOW) mode1.

Option E, combination of the Amazon SageMaker BlazingText algorithm in Batch Skip-gram mode with a custom recurrent neural network (RNN), is another valid option because it can leverage the advantages of Skip-gram mode and also use an RNN to model the sequential nature of the text. An RNN can capture the temporal dependencies and long-term dependencies between words, which are important for QA tasks2.

Option A, Amazon SageMaker seq2seq algorithm, is not a valid option because it is designed for sequence-to-sequence tasks such as machine translation, summarization, or chatbots. It does not produce embedding vectors for text series, but rather generates an output sequence given an input sequence3.

Option C, Amazon SageMaker Object2Vec algorithm, is not a valid option because it is designed for learning embeddings for pairs of objects, such as text-image, text-text, or image-image. It does not produce embedding vectors for text series, but rather learns a similarity function between pairs of objects4.

Option D, Amazon SageMaker BlazingText algorithm in continuous bag-of-words (CBOW) mode, is not a valid option because it does not capture word context as well as Skip-gram mode. CBOW mode predicts a word given its surrounding words, while Skip-gram mode predicts the surrounding words given a word. CBOW mode is faster and more suitable for frequent words, but Skip-gram mode can learn more meaningful embeddings for rare words1.

1: Amazon SageMaker BlazingText

2: Recurrent Neural Networks (RNNs)

3: Amazon SageMaker Seq2Seq

4: Amazon SageMaker Object2Vec