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Amazon AWS Certified Machine Learning - Specialty Sample Questions (Q49-Q54):

NEW QUESTION # 49
A global financial company is using machine learning to automate its loan approval process. The company has a dataset of customer information. The dataset contains some categorical fields, such as customer location by city and housing status. The dataset also includes financial fields in different units, such as account balances in US dollars and monthly interest in US cents.
The company's data scientists are using a gradient boosting regression model to infer the credit score for each customer. The model has a training accuracy of 99% and a testing accuracy of 75%. The data scientists want to improve the model's testing accuracy.
Which process will improve the testing accuracy the MOST?

• A. Use a label encoder for the categorical fields in the dataset. Perform L1 regularization on the financial fields in the dataset. Apply L2 regularization to the data.
• B. Use a one-hot encoder for the categorical fields in the dataset. Perform standardization on the financial fields in the dataset. Apply L1 regularization to the data.
• C. Use tokenization of the categorical fields in the dataset. Perform binning on the financial fields in the dataset. Remove the outliers in the data by using the z-score.
• D. Use a logarithm transformation on the categorical fields in the dataset. Perform binning on the financial fields in the dataset. Use imputation to populate missing values in the dataset.

Answer: B

Explanation:
The question is about improving the testing accuracy of a gradient boosting regression model. The testing accuracy is much lower than the training accuracy, which indicates that the model is overfitting the training data. To reduce overfitting, the following steps are recommended:
Use a one-hot encoder for the categorical fields in the dataset. This will create binary features for each category and avoid imposing an ordinal relationship among them. This can help the model learn the patterns better and generalize to unseen data.
Perform standardization on the financial fields in the dataset. This will scale the features to have zero mean and unit variance, which can improve the convergence and performance of the model. This can also help the model handle features with different units and ranges.
Apply L1 regularization to the data. This will add a penalty term to the loss function that is proportional to the absolute value of the coefficients. This can help the model reduce the complexity and select the most relevant features by shrinking the coefficients of less important features to zero.
References:
1: AWS Machine Learning Specialty Exam Guide
2: AWS Machine Learning Specialty Course
3: AWS Machine Learning Blog

 

NEW QUESTION # 50
A manufacturer is operating a large number of factories with a complex supply chain relationship where unexpected downtime of a machine can cause production to stop at several factories. A data scientist wants to analyze sensor data from the factories to identify equipment in need of preemptive maintenance and then dispatch a service team to prevent unplanned downtime. The sensor readings from a single machine can include up to 200 data points including temperatures, voltages, vibrations, RPMs, and pressure readings.
To collect this sensor data, the manufacturer deployed Wi-Fi and LANs across the factories. Even though many factory locations do not have reliable or high-speed internet connectivity, the manufacturer would like to maintain near-real-time inference capabilities.
Which deployment architecture for the model will address these business requirements?

• A. Deploy the model on AWS IoT Greengrass in each factory. Run sensor data through this model to infer which machines need maintenance.
• B. Deploy the model in Amazon SageMaker and use an IoT rule to write data to an Amazon DynamoDB table. Consume a DynamoDB stream from the table with an AWS Lambda function to invoke the endpoint.
• C. Deploy the model in Amazon SageMaker. Run sensor data through this model to predict which machines need maintenance.
• D. Deploy the model to an Amazon SageMaker batch transformation job. Generate inferences in a daily batch report to identify machines that need maintenance.

Answer: A

Explanation:
AWS IoT Greengrass is a service that extends AWS to edge devices, such as sensors and machines, so they can act locally on the data they generate, while still using the cloud for management, analytics, and durable storage. AWS IoT Greengrass enables local device messaging, secure data transfer, and local computing using AWS Lambda functions and machine learning models. AWS IoT Greengrass can run machine learning inference locally on devices using models that are created and trained in the cloud. This allows devices to respond quickly to local events, even when they are offline or have intermittent connectivity. Therefore, option B is the best deployment architecture for the model to address the business requirements of the manufacturer.
Option A is incorrect because deploying the model in Amazon SageMaker would require sending the sensor data to the cloud for inference, which would not work well for factory locations that do not have reliable or high-speed internet connectivity. Moreover, this option would not provide near-real-time inference capabilities, as there would be latency and bandwidth issues involved in transferring the data to and from the cloud. Option C is incorrect because deploying the model to an Amazon SageMaker batch transformation job would not provide near-real-time inference capabilities, as batch transformation is an asynchronous process that operates on large datasets. Batch transformation is not suitable for streaming data that requires low- latency responses. Option D is incorrect because deploying the model in Amazon SageMaker and using an IoT rule to write data to an Amazon DynamoDB table would also require sending the sensor data to the cloud for inference, which would have the same drawbacks as option A. Moreover, this option would introduce additional complexity and cost by involving multiple services, such as IoT Core, DynamoDB, and Lambda.
AWS Greengrass Machine Learning Inference - Amazon Web Services
Machine learning components - AWS IoT Greengrass
What is AWS Greengrass? | AWS IoT Core | Onica
GitHub - aws-samples/aws-greengrass-ml-deployment-sample
AWS IoT Greengrass Architecture and Its Benefits | Quick Guide - XenonStack

 

NEW QUESTION # 51
An insurance company is developing a new device for vehicles that uses a camera to observe drivers' behavior and alert them when they appear distracted The company created approximately 10,000 training images in a controlled environment that a Machine Learning Specialist will use to train and evaluate machine learning models During the model evaluation the Specialist notices that the training error rate diminishes faster as the number of epochs increases and the model is not accurately inferring on the unseen test images Which of the following should be used to resolve this issue? (Select TWO)

• A. Add L2 regularization to the model
• B. Add vanishing gradient to the model
• C. Perform data augmentation on the training data
• D. Use gradient checking in the model
• E. Make the neural network architecture complex.

Answer: B,C

 

NEW QUESTION # 52
A company is using Amazon Textract to extract textual data from thousands of scanned text-heavy legal documents daily. The company uses this information to process loan applications automatically. Some of the documents fail business validation and are returned to human reviewers, who investigate the errors. This activity increases the time to process the loan applications.
What should the company do to reduce the processing time of loan applications?

• A. Configure Amazon Textract to route low-confidence predictions to Amazon Augmented AI (Amazon A2I). Perform a manual review on those words before performing a business validation.
• B. Use Amazon Rekognition's feature to detect text in an image to extract the data from scanned images. Use this information to process the loan applications.
• C. Configure Amazon Textract to route low-confidence predictions to Amazon SageMaker Ground Truth. Perform a manual review on those words before performing a business validation.
• D. Use an Amazon Textract synchronous operation instead of an asynchronous operation.

Answer: A

Explanation:
The company should configure Amazon Textract to route low-confidence predictions to Amazon Augmented AI (Amazon A2I). Amazon A2I is a service that allows you to implement human review of machine learning (ML) predictions. It also comes integrated with some of the Artificial Intelligence (AI) services such as Amazon Textract. By using Amazon A2I, the company can perform a manual review on those words that have low confidence scores before performing a business validation. This will help reduce the processing time of loan applications by avoiding errors and rework.
Option A is incorrect because Amazon SageMaker Ground Truth is not a suitable service for human review of Amazon Textract predictions. Amazon SageMaker Ground Truth is a service that helps you build highly accurate training datasets for machine learning. It allows you to label your own data or use a workforce of human labelers. However, it does not provide an easy way to integrate with Amazon Textract and route low-confidence predictions for human review.
Option B is incorrect because using an Amazon Textract synchronous operation instead of an asynchronous operation will not reduce the processing time of loan applications. A synchronous operation is a request-response operation that returns the results immediately. An asynchronous operation is a start-and-check operation that returns a job identifier that you can use to check the status and results later. The choice of operation depends on the size and complexity of the document, not on the confidence of the predictions.
Option D is incorrect because using Amazon Rekognition's feature to detect text in an image to extract the data from scanned images is not a better alternative than using Amazon Textract. Amazon Rekognition is a service that provides computer vision capabilities, such as face recognition, object detection, and scene analysis. It can also detect text in an image, but it does not provide the same level of accuracy and functionality as Amazon Textract. Amazon Textract can not only detect text, but also extract data from tables and forms, and understand the layout and structure of the document.
References:
Amazon Augmented AI
Amazon SageMaker Ground Truth
Amazon Textract Operations
Amazon Rekognition

 

NEW QUESTION # 53
A Data Scientist is developing a binary classifier to predict whether a patient has a particular disease on a series of test results. The Data Scientist has data on 400 patients randomly selected from the population. The disease is seen in 3% of the population.
Which cross-validation strategy should the Data Scientist adopt?

• A. An 80/20 stratified split between training and validation
• B. A k-fold cross-validation strategy with k=5 and 3 repeats
• C. A k-fold cross-validation strategy with k=5
• D. A stratified k-fold cross-validation strategy with k=5

Answer: D

 

NEW QUESTION # 54
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