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MLS-C01 · Question #163

MLS-C01 Question #163: Real Exam Question with Answer & Explanation

The correct answer is B: Deploy AWS DeepLens cameras in the restaurant to capture video.. B is correct with Rekognition integrated with Deeplens and no extra configuration needed. https://aws.amazon.com/blogs/machine-learning/building-a-smart-garage-door-opener-with-aws- deeplens-and-amazon-rekognition/

Machine Learning Implementation and Operations

Question

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?

Options

  • AInstall cameras compatible with Amazon Kinesis Video Streams to stream the data to AWS over
  • BDeploy AWS DeepLens cameras in the restaurant to capture video.
  • CBuild a custom model in Amazon SageMaker to recognize the number of people in an image.
  • DBuild a custom model in Amazon SageMaker to recognize the number of people in an image.

Explanation

B is correct with Rekognition integrated with Deeplens and no extra configuration needed. https://aws.amazon.com/blogs/machine-learning/building-a-smart-garage-door-opener-with-aws- deeplens-and-amazon-rekognition/

Topics

#Edge computing#AWS DeepLens#ML Inference#Bandwidth constraints

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