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NEW QUESTION # 112
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? (Choose two.)
Answer: B,E
Explanation:
The model must have been overfitted. Regularization helps to solve the overfitting problem in machine learning (as well as data augmentation).
NEW QUESTION # 113
A Machine Learning Specialist discover the following statistics while experimenting on a model.
What can the Specialist from the experiments?
Answer: C
Explanation:
The model in Experiment 1 had a high variance error because it performed well on the training data (train error = 5%) but poorly on the test data (test error = 8%). This indicates that the model was overfitting the training data and not generalizing well to new data. The model in Experiment 3 had a lower variance error because it performed similarly on the training data (train error = 5.1%) and the test data (test error = 5.4%). This indicates that the model was more robust and less sensitive to the fluctuations in the training data. The model in Experiment 3 achieved this improvement by implementing regularization, which is a technique that reduces the complexity of the model and prevents overfitting by adding a penalty term to the loss function. The model in Experiment 2 had a minimal bias error because it performed similarly on the training data (train error = 5.2%) and the test data (test error = 5.7%) as the model in Experiment 1. This indicates that the model was not underfitting the data and capturing the true relationship between the input and output variables. The model in Experiment 2 increased the number of layers and neurons in the model, which is a way to increase the complexity and flexibility of the model. However, this did not improve the performance of the model, as the variance error remained high. This shows that increasing the complexity of the model is not always the best way to reduce the bias error, and may even increase the variance error if the model becomes too complex for the data. References:
Bias Variance Tradeoff - Clearly Explained - Machine Learning Plus
The Bias-Variance Trade-off in Machine Learning - Stack Abuse
NEW QUESTION # 114
A Machine Learning Specialist is packaging a custom ResNet model into a Docker container so the company can leverage Amazon SageMaker for training. The Specialist is using Amazon EC2 P3 instances to train the model and needs to properly configure the Docker container to leverage the NVIDIA GPUs.
What does the Specialist need to do?
Answer: D
NEW QUESTION # 115
A machine learning specialist needs to analyze comments on a news website with users across the globe. The specialist must find the most discussed topics in the comments that are in either English or Spanish.
What steps could be used to accomplish this task? (Choose two.)
Answer: A
NEW QUESTION # 116
A manufacturing company uses machine learning (ML) models to detect quality issues. The models use images that are taken of the company's product at the end of each production step. The company has thousands of machines at the production site that generate one image per second on average.
The company ran a successful pilot with a single manufacturing machine. For the pilot, ML specialists used an industrial PC that ran AWS IoT Greengrass with a long-running AWS Lambda function that uploaded the images to Amazon S3. The uploaded images invoked a Lambda function that was written in Python to perform inference by using an Amazon SageMaker endpoint that ran a custom model. The inference results were forwarded back to a web service that was hosted at the production site to prevent faulty products from being shipped.
The company scaled the solution out to all manufacturing machines by installing similarly configured industrial PCs on each production machine. However, latency for predictions increased beyond acceptable limits. Analysis shows that the internet connection is at its capacity limit.
How can the company resolve this issue MOST cost-effectively?
Answer: A
Explanation:
The best option is to deploy the Lambda function and the ML models onto the AWS IoT Greengrass core that is running on the industrial PCs that are installed on each machine. This way, the inference can be performed locally on the edge devices, without the need to upload the images to Amazon S3 and invoke the SageMaker endpoint. This will reduce the latency and the network bandwidth consumption. The long-running Lambda function can be extended to invoke the Lambda function with the captured images and run the inference on the edge component that forwards the results directly to the web service. This will also simplify the architecture and eliminate the dependency on the internet connection.
Option A is not cost-effective, as it requires setting up a 10 Gbps AWS Direct Connect connection and increasing the size and number of instances for the SageMaker endpoint. This will increase the operational costs and complexity.
Option B is not optimal, as it still requires uploading the images to Amazon S3 and invoking the SageMaker endpoint. Compressing and decompressing the images will add additional processing overhead and latency.
Option C is not sufficient, as it still requires uploading the images to Amazon S3 and invoking the SageMaker endpoint. Auto scaling for SageMaker will help to handle the increased workload, but it will not reduce the latency or the network bandwidth consumption. Setting up an AWS Direct Connect connection will improve the network performance, but it will also increase the operational costs and complexity. References:
AWS IoT Greengrass
Deploying Machine Learning Models to Edge Devices
AWS Certified Machine Learning - Specialty Exam Guide
NEW QUESTION # 117
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