AWS CERTIFIED MACHINE LEARNING - SPECIALTY VCE FILES, VALID FREE AMAZON MLS-C01 VCE DUMPS

AWS Certified Machine Learning - Specialty vce files, valid free Amazon MLS-C01 vce dumps

AWS Certified Machine Learning - Specialty vce files, valid free Amazon MLS-C01 vce dumps

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The MLS-C01 Exam covers a range of topics, including machine learning and deep learning algorithms, data modeling and evaluation, and data visualization. Candidates will be tested on their ability to design, implement, and maintain scalable, cost-effective, and highly available machine learning solutions on AWS. Candidates will also be expected to demonstrate their knowledge of AWS services such as Amazon SageMaker, Amazon Rekognition, Amazon Comprehend, and Amazon Personalize.

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Amazon AWS Certified Machine Learning - Specialty Sample Questions (Q42-Q47):

NEW QUESTION # 42
A manufacturing company has a production line with sensors that collect hundreds of quality metrics. The company has stored sensor data and manual inspection results in a data lake for several months. To automate quality control, the machine learning team must build an automated mechanism that determines whether the produced goods are good quality, replacement market quality, or scrap quality based on the manual inspection results.
Which modeling approach will deliver the MOST accurate prediction of product quality?

  • A. Amazon SageMaker Latent Dirichlet Allocation (LDA) algorithm
  • B. Amazon SageMaker DeepAR forecasting algorithm
  • C. Amazon SageMaker XGBoost algorithm
  • D. A convolutional neural network (CNN) and ResNet

Answer: D

Explanation:
A convolutional neural network (CNN) is a type of deep learning model that can learn to extract features from images and perform tasks such as classification, segmentation, and detection1. ResNet is a popular CNN architecture that uses residual connections to overcome the problem of vanishing gradients and enable very deep networks2. For the task of predicting product quality based on sensor data, a CNN and ResNet approach can leverage the spatial structure of the data and learn complex patterns that distinguish different quality levels.
References:
Convolutional Neural Networks (CNNs / ConvNets)
PyTorch ResNet: The Basics and a Quick Tutorial


NEW QUESTION # 43
A company uses a long short-term memory (LSTM) model to evaluate the risk factors of a particular energy sector. The model reviews multi-page text documents to analyze each sentence of the text and categorize it as either a potential risk or no risk. The model is not performing well, even though the Data Scientist has experimented with many different network structures and tuned the corresponding hyperparameters.
Which approach will provide the MAXIMUM performance boost?

  • A. Initialize the words by word2vec embeddings pretrained on a large collection of news articles related to the energy sector.
  • B. Use gated recurrent units (GRUs) instead of LSTM and run the training process until the validation loss stops decreasing.
  • C. Reduce the learning rate and run the training process until the training loss stops decreasing.
  • D. Initialize the words by term frequency-inverse document frequency (TF-IDF) vectors pretrained on a large collection of news articles related to the energy sector.

Answer: A

Explanation:
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


NEW QUESTION # 44
A library is developing an automatic book-borrowing system that uses Amazon Rekognition. Images of library members' faces are stored in an Amazon S3 bucket. When members borrow books, the Amazon Rekognition CompareFaces API operation compares real faces against the stored faces in Amazon S3.
The library needs to improve security by making sure that images are encrypted at rest. Also, when the images are used with Amazon Rekognition. they need to be encrypted in transit. The library also must ensure that the images are not used to improve Amazon Rekognition as a service.
How should a machine learning specialist architect the solution to satisfy these requirements?

  • A. Switch to using an Amazon Rekognition collection to store the images. Use the IndexFaces and SearchFacesByImage API operations instead of the CompareFaces API operation.
  • B. Switch to using the AWS GovCloud (US) Region for Amazon S3 to store images and for Amazon Rekognition to compare faces. Set up a VPN connection and only call the Amazon Rekognition API operations through the VPN.
  • C. Enable client-side encryption on the S3 bucket. Set up a VPN connection and only call the Amazon Rekognition API operations through the VPN.
  • D. Enable server-side encryption on the S3 bucket. Submit an AWS Support ticket to opt out of allowing images to be used for improving the service, and follow the process provided by AWS Support.

Answer: D

Explanation:
The best solution for encrypting images at rest and in transit, and opting out of data usage for service improvement, is to use the following steps:
Enable server-side encryption on the S3 bucket. This will encrypt the images stored in the bucket using AWS Key Management Service (AWS KMS) customer master keys (CMKs). This will protect the data at rest from unauthorized access1 Submit an AWS Support ticket to opt out of allowing images to be used for improving the service, and follow the process provided by AWS Support. This will prevent AWS from storing or using the images processed by Amazon Rekognition for service development or enhancement purposes. This will protect the data privacy and ownership2 Use HTTPS to call the Amazon Rekognition CompareFaces API operation. This will encrypt the data in transit between the client and the server using SSL/TLS protocols. This will protect the data from interception or tampering3 The other options are incorrect because they either do not encrypt the images at rest or in transit, or do not opt out of data usage for service improvement. For example:
Option B switches to using an Amazon Rekognition collection to store the images. A collection is a container for storing face vectors that are calculated by Amazon Rekognition. It does not encrypt the images at rest or in transit, and it does not opt out of data usage for service improvement. It also requires changing the API operations from CompareFaces to IndexFaces and SearchFacesByImage, which may not have the same functionality or performance4 Option C switches to using the AWS GovCloud (US) Region for Amazon S3 and Amazon Rekognition. The AWS GovCloud (US) Region is an isolated AWS Region designed to host sensitive data and regulated workloads in the cloud. It does not automatically encrypt the images at rest or in transit, and it does not opt out of data usage for service improvement. It also requires migrating the data and the application to a different Region, which may incur additional costs and complexity5 Option D enables client-side encryption on the S3 bucket. This means that the client is responsible for encrypting and decrypting the images before uploading or downloading them from the bucket. This adds extra overhead and complexity to the client application, and it does not encrypt the data in transit when calling the Amazon Rekognition API. It also does not opt out of data usage for service improvement.
1: Protecting Data Using Server-Side Encryption with AWS KMS-Managed Keys (SSE-KMS) - Amazon Simple Storage Service
2: Opting Out of Content Storage and Use for Service Improvements - Amazon Rekognition
3: HTTPS - Wikipedia
4: Working with Stored Faces - Amazon Rekognition
5: AWS GovCloud (US) - Amazon Web Services
Protecting Data Using Client-Side Encryption - Amazon Simple Storage Service


NEW QUESTION # 45
A company wants to predict the sale prices of houses based on available historical sales dat a. The target variable in the company's dataset is the sale price. The features include parameters such as the lot size, living area measurements, non-living area measurements, number of bedrooms, number of bathrooms, year built, and postal code. The company wants to use multi-variable linear regression to predict house sale prices.
Which step should a machine learning specialist take to remove features that are irrelevant for the analysis and reduce the model's complexity?

  • A. Build a heatmap showing the correlation of the dataset against itself. Remove features with low mutual correlation scores.
  • B. Plot a histogram of the features and compute their standard deviation. Remove features with high variance.
  • C. Run a correlation check of all features against the target variable. Remove features with low target variable correlation scores.
  • D. Plot a histogram of the features and compute their standard deviation. Remove features with low variance.

Answer: C

Explanation:
Feature selection is the process of reducing the number of input variables to those that are most relevant for predicting the target variable. One way to do this is to run a correlation check of all features against the target variable and remove features with low target variable correlation scores. This means that these features have little or no linear relationship with the target variable and are not useful for the prediction. This can reduce the model's complexity and improve its performance. References:
Feature engineering - Machine Learning Lens
Feature Selection For Machine Learning in Python


NEW QUESTION # 46
A Machine Learning Specialist built an image classification deep learning model. However the Specialist ran into an overfitting problem in which the training and testing accuracies were 99% and 75%r respectively.
How should the Specialist address this issue and what is the reason behind it?

  • A. The dimensionality of dense layer next to the flatten layer should be increased because the model is not complex enough.
  • B. The epoch number should be increased because the optimization process was terminated before it reached the global minimum.
  • C. The dropout rate at the flatten layer should be increased because the model is not generalized enough.
  • D. The learning rate should be increased because the optimization process was trapped at a local minimum.

Answer: D


NEW QUESTION # 47
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