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The AWS Certified Machine Learning - Specialty exam is a certification offered by Amazon Web Services (AWS) for professionals who want to validate their expertise in machine learning. MLS-C01 exam is designed for individuals who have a solid understanding of machine learning concepts and techniques, as well as experience using AWS services to build and deploy machine learning solutions. MLS-C01 Exam covers a range of topics, including data preparation, model training and evaluation, deployment and implementation, and automation.
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Amazon AWS Certified Machine Learning - Specialty Sample Questions (Q217-Q222):
NEW QUESTION # 217
For the given confusion matrix, what is the recall and precision of the model?
Answer: C
NEW QUESTION # 218
A Machine Learning Specialist is building a prediction model for a large number of features using linear models, such as linear regression and logistic regression During exploratory data analysis the Specialist observes that many features are highly correlated with each other This may make the model unstable What should be done to reduce the impact of having such a large number of features?
Answer: A
NEW QUESTION # 219
A Machine Learning team uses Amazon SageMaker to train an Apache MXNet handwritten digit classifier model using a research dataset. The team wants to receive a notification when the model is overfitting.
Auditors want to view the Amazon SageMaker log activity report to ensure there are no unauthorized API calls.
What should the Machine Learning team do to address the requirements with the least amount of code and fewest steps?
Answer: B
NEW QUESTION # 220
A company is building a line-counting application for use in a quick-service restaurant. The company wants to use video cameras pointed at the line of customers at a given register to measure how many people are in line and deliver notifications to managers if the line grows too long. The restaurant locations have limited bandwidth for connections to external services and cannot accommodate multiple video streams without impacting other operations.
Which solution should a machine learning specialist implement to meet these requirements?
Answer: D
Explanation:
The best solution for building a line-counting application for use in a quick-service restaurant is to use the following steps:
* Build a custom model in Amazon SageMaker to recognize the number of people in an image. Amazon SageMaker is a fully managed service that provides tools and workflows for building, training, and deploying machine learning models. A custom model can be tailored to the specific use case of line- counting and achieve higher accuracy than a generic model1
* Deploy AWS DeepLens cameras in the restaurant to capture video. AWS DeepLens is a wireless video camera that integrates with Amazon SageMaker and AWS Lambda. It can run machine learning inference locally on the device without requiring internet connectivity or streaming video to the cloud. This reduces the bandwidth consumption and latency of the application2
* Deploy the model to the cameras. AWS DeepLens allows users to deploy trained models from Amazon SageMaker to the cameras with a few clicks. The cameras can then use the model to process the video frames and count the number of people in each frame2
* Deploy an AWS Lambda function to the cameras to use the model to count people and send an Amazon Simple Notification Service (Amazon SNS) notification if the line is too long. AWS Lambda is a serverless computing service that lets users run code without provisioning or managing servers. AWS DeepLens supports running Lambda functions on the device to perform actions based on the inference results. Amazon SNS is a service that enables users to send notifications to subscribers via email, SMS, or mobile push23 The other options are incorrect because they either require internet connectivity or streaming video to the cloud, which may impact the bandwidth and performance of the application. For example:
* Option A uses Amazon Kinesis Video Streams to stream the data to AWS over the restaurant's existing internet connection. Amazon Kinesis Video Streams is a service that enables users to capture, process, and store video streams for analytics and machine learning. However, this option requires streaming multiple video streams to the cloud, which may consume a lot of bandwidth and cause network congestion. It also requires internet connectivity, which may not be reliable or available in some locations4
* Option B uses Amazon Rekognition on the AWS DeepLens device. Amazon Rekognition is a service that provides computer vision capabilities, such as face detection, face recognition, and object detection. However, this option requires calling the Amazon Rekognition API over the internet, which may introduce latency and require bandwidth. It also uses a generic face detection model, which may not be optimized for the line-counting use case.
* Option C uses Amazon SageMaker to build a custom model and an Amazon SageMaker endpoint to call the model. Amazon SageMaker endpoints are hosted web services that allow users to perform inference on their models. However, this option requires sending the images to the endpoint over the internet, which may consume bandwidth and introduce latency. It also requires internet connectivity, which may not be reliable or available in some locations.
References:
* 1: Amazon SageMaker - Machine Learning Service - AWS
* 2: AWS DeepLens - Deep learning enabled video camera - AWS
* 3: Amazon Simple Notification Service (SNS) - AWS
* 4: Amazon Kinesis Video Streams - Amazon Web Services
* : Amazon Rekognition - Video and Image - AWS
* : Deploy a Model - Amazon SageMaker
NEW QUESTION # 221
This graph shows the training and validation loss against the epochs for a neural network The network being trained is as follows
* Two dense layers one output neuron
* 100 neurons in each layer
* 100 epochs
* Random initialization of weights
Which technique can be used to improve model performance in terms of accuracy in the validation set?
Answer: D
NEW QUESTION # 222
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