Changed MLS-C01 Exam Questions

Page: 14 / 22

Question 56

A company wants to classify user behavior as either fraudulent or normal. Based on internal research, a Machine Learning Specialist would like to build a binary classifier based on two features: age of account and transaction month. The class distribution for these features is illustrated in the figure provided.

Based on this information which model would have the HIGHEST accuracy?

Options:

Long short-term memory (LSTM) model with scaled exponential linear unit (SELL))

Logistic regression

Support vector machine (SVM) with non-linear kernel

Single perceptron with tanh activation function

Question 57

A machine learning (ML) engineer is preparing a dataset for a classification model. The ML engineer notices that some continuous numeric features have a significantly greater value than most other features. A business expert explains that the features are independently informative and that the dataset is representative of the target distribution.

After training, the model's inferences accuracy is lower than expected.

Which preprocessing technique will result in the GREATEST increase of the model's inference accuracy?

Options:

Normalize the problematic features.

Bootstrap the problematic features.

Remove the problematic features.

Extrapolate synthetic features.

Question 58

A company is converting a large number of unstructured paper receipts into images. The company wants to create a model based on natural language processing (NLP) to find relevant entities such as date, location, and notes, as well as some custom entities such as receipt numbers.

The company is using optical character recognition (OCR) to extract text for data labeling. However, documents are in different structures and formats, and the company is facing challenges with setting up the manual workflows for each document type. Additionally, the company trained a named entity recognition (NER) model for custom entity detection using a small sample size. This model has a very low confidence score and will require retraining with a large dataset.

Which solution for text extraction and entity detection will require the LEAST amount of effort?

Options:

Extract text from receipt images by using Amazon Textract. Use the Amazon SageMaker BlazingText algorithm to train on the text for entities and custom entities.

Extract text from receipt images by using a deep learning OCR model from the AWS Marketplace. Use the NER deep learning model to extract entities.

Extract text from receipt images by using Amazon Textract. Use Amazon Comprehend for entity detection, and use Amazon Comprehend custom entity recognition for custom entity detection.

Extract text from receipt images by using a deep learning OCR model from the AWS Marketplace. Use Amazon Comprehend for entity detection, and use Amazon Comprehend custom entity recognition for custom entity detection.

Answer:

Explanation:

The best solution for text extraction and entity detection with the least amount of effort is to use Amazon Textract and Amazon Comprehend. These services are:

Amazon Textract for text extraction from receipt images. Amazon Textract is a machine learning service that can automatically extract text and data from scanned documents. It can handle different structures and formats of documents, such as PDF, TIFF, PNG, and JPEG, without any preprocessing steps. It can also extract key-value pairs and tables from documents1

Amazon Comprehend for entity detection and custom entity detection. Amazon Comprehend is a natural language processing service that can identify entities, such as dates, locations, and notes, from unstructured text. It can also detect custom entities, such as receipt numbers, by using a custom entity recognizer that can be trained with a small amount of labeled data2

The other options are not suitable because they either require more effort for text extraction, entity detection, or custom entity detection. For example:

Option A uses the Amazon SageMaker BlazingText algorithm to train on the text for entities and custom entities. BlazingText is a supervised learning algorithm that can perform text classification and word2vec. It requires users to provide a large amount of labeled data, preprocess the data into a specific format, and tune the hyperparameters of the model3

Option B uses a deep learning OCR model from the AWS Marketplace and a NER deep learning model for text extraction and entity detection. These models are pre-trained and may not be suitable for the specific use case of receipt processing. They also require users to deploy and manage the models on Amazon SageMaker or Amazon EC2 instances4

Option D uses a deep learning OCR model from the AWS Marketplace for text extraction. This model has the same drawbacks as option B. It also requires users to integrate the model output with Amazon Comprehend for entity detection and custom entity detection.

References:

1: Amazon Textract – Extract text and data from documents

2: Amazon Comprehend – Natural Language Processing (NLP) and Machine Learning (ML)

3: BlazingText - Amazon SageMaker

4: AWS Marketplace: OCR

Question 59

A retail chain has been ingesting purchasing records from its network of 20,000 stores to Amazon S3 using Amazon Kinesis Data Firehose To support training an improved machine learning model, training records will require new but simple transformations, and some attributes will be combined The model needs lo be retrained daily

Given the large number of stores and the legacy data ingestion, which change will require the LEAST amount of development effort?

Options:

Require that the stores to switch to capturing their data locally on AWS Storage Gateway for loading into Amazon S3 then use AWS Glue to do the transformation

Deploy an Amazon EMR cluster running Apache Spark with the transformation logic, and have the cluster run each day on the accumulating records in Amazon S3, outputting new/transformed records to Amazon S3

Spin up a fleet of Amazon EC2 instances with the transformation logic, have them transform the data records accumulating on Amazon S3, and output the transformed records to Amazon S3.

Insert an Amazon Kinesis Data Analytics stream downstream of the Kinesis Data Firehouse stream that transforms raw record attributes into simple transformed values using SQL.