Instant Access Amazon.AWS-Certified-Machine-Learning-Specialty.v2024-06-17.q246 Actual Practice Test Engine for Free (Page 15)

Question 66

An office security agency conducted a successful pilot using 100 cameras installed at key locations within the main office. Images from the cameras were uploaded to Amazon S3 and tagged using Amazon Rekognition, and the results were stored in Amazon ES. The agency is now looking to expand the pilot into a full production system using thousands of video cameras in its office locations globally. The goal is to identify activities performed by non-employees in real time.
Which solution should the agency consider?

A.Install AWS DeepLens cameras and use the DeepLens_Kinesis_video module to stream video to Amazon Kinesis Video Streams for each camera. On each stream, run an AWS Lambda function to capture image fragments and then call Amazon Rekognition Image to detect faces from a collection of known employees, and alert when non-employees are detected.
B.Install AWS DeepLens cameras and use the DeepLens_Kinesis_video module to stream video to Amazon Kinesis Video Streams for each camera. On each stream, use Amazon Rekognition Video and create a stream processor to detect faces from a collection on each stream, and alert when non-employees are detected.
C.Use a proxy server at each local office and for each camera, and stream the RTSP feed to a unique Amazon Kinesis Video Streams video stream. On each stream, use Amazon Rekognition Video and create a stream processor to detect faces from a collection of known employees, and alert when non-employees are detected
D.Use a proxy server at each local office and for each camera, and stream the RTSP feed to a unique Amazon Kinesis Video Streams video strearw On each stream, use Amazon Rekognition Image to detect faces from a collection of known employees and alert when non-employees are detected.

Question 67

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 term frequency-inverse document frequency (TF-IDF) vectors 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 word2vec embeddings pretrained on a large collection of news articles related to the energy sector.

Question 68

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?

A.Single perceptron with tanh activation function
B.Support vector machine (SVM) with non-linear kernel
C.Logistic regression
D.Long short-term memory (LSTM) model with scaled exponential linear unit (SELL))

Question 69

A company is creating an application to identify, count, and classify animal images that are uploaded to the company's website. The company is using the Amazon SageMaker image classification algorithm with an ImageNetV2 convolutional neural network (CNN). The solution works well for most animal images but does not recognize many animal species that are less common.
The company obtains 10,000 labeled images of less common animal species and stores the images in Amazon S3. A machine learning (ML) engineer needs to incorporate the images into the model by using Pipe mode in SageMaker.
Which combination of steps should the ML engineer take to train the model? (Choose two.)

A.Use a ResNet model. Initiate full training mode by initializing the network with random weights.
B.Use an Inception model that is available with the SageMaker image classification algorithm.
C.Create a .lst file that contains a list of image files and corresponding class labels. Upload the .lst file to Amazon S3.
D.Initiate transfer learning. Train the model by using the images of less common species.
E.Use an augmented manifest file in JSON Lines format.

Correct Answer: C,D

Explanation
The combination of steps that the ML engineer should take to train the model are to create a .lst file that contains a list of image files and corresponding class labels, upload the .lst file to Amazon S3, and initiate transfer learning by training the model using the images of less common species. This approach will allow the ML engineer to leverage the existing ImageNetV2 CNN model and fine-tune it with the new data using Pipe mode in SageMaker.
A .lst file is a text file that contains a list of image files and corresponding class labels, separated by tabs. The
.lst file format is required for using the SageMaker image classification algorithm with Pipe mode. Pipe mode is a feature of SageMaker that enables streaming data directly from Amazon S3 to the training instances, without downloading the data first. Pipe mode can reduce the startup time, improve the I/O throughput, and enable training on large datasets that exceed the disk size limit. To use Pipe mode, the ML engineer needs to upload the .lst file to Amazon S3 and specify the S3 path as the input data channel for the training job1.
Transfer learning is a technique that enables reusing a pre-trained model for a new task by fine-tuning the model parameters with new data. Transfer learning can save time and computational resources, as well as improve the performance of the model, especially when the new task is similar to the original task. The SageMaker image classification algorithm supports transfer learning by allowing the ML engineer to specify the number of output classes and the number of layers to be retrained. The ML engineer can use the existing ImageNetV2 CNN model, which is trained on 1,000 classes of common objects, and fine-tune it with the new data of less common animal species, which is a similar task2.
The other options are either less effective or not supported by the SageMaker image classification algorithm.
Using a ResNet model and initiating full training mode would require training the model from scratch, which would take more time and resources than transfer learning. Using an Inception model is not possible, as the SageMaker image classification algorithm only supports ResNet and ImageNetV2 models. Using an augmented manifest file in JSON Lines format is not compatible with Pipe mode, as Pipe mode only supports
.lst files for image classification1.
References:
1: Using Pipe input mode for Amazon SageMaker algorithms | AWS Machine Learning Blog
2: Image Classification Algorithm - Amazon SageMaker

Question 70

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.Use gradient checking in the model
D.Perform data augmentation on the training data
E.Make the neural network architecture complex.

Question 66

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Question 70

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