Amazon Web Services MLS-C01 Exam With Confidence Using Practice Dumps

 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.

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

 The modeling technique that the Machine Learning Specialist should use is binary classification. Binary classification is a type of supervised learning that predicts whether an input belongs to one of two possible classes. In this case, the input is the customer’s recent usage data and the output is whether or not the customer is likely to switch to a competitor in the next 30 days. This is a binary outcome, either yes or no, so binary classification is suitable for this problem. The other options are not appropriate for this problem. Time-series prediction is a type of supervised learning that forecasts future values based on past and present data. Anomaly detection is a type of unsupervised learning that identifies outliers or abnormal patterns in the data. Regression is a type of supervised learning that estimates a continuous numerical value based on the input features. References: Binary Classification, Time Series Prediction, Anomaly Detection, Regression

 The best solution to reduce the cost of the notebook instance and the data preprocessing job is to change the notebook instance type to a smaller general-purpose instance, stop the notebook when it is not in use, and run data preprocessing on an ml.r5 instance with the same memory size as the ml.m5.4xlarge instance by using Amazon SageMaker Processing. This solution will result in the most cost savings because:

Changing the notebook instance type to a smaller general-purpose instance will reduce the hourly cost of running the notebook, since the feature engineering development does not require high CPU and memory resources. For example, an ml.t3.medium instance costs $0.0464 per hour, while an ml.m5.4xlarge instance costs $0.888 per hour1.

Stopping the notebook when it is not in use will also reduce the cost, since the notebook will only incur charges when it is running. For example, if the notebook is used for 8 hours per day, 5 days per week, then stopping it when it is not in use will save about 76% of the monthly cost compared to leaving it running all the time2.

Running data preprocessing on an ml.r5 instance with the same memory size as the ml.m5.4xlarge instance by using Amazon SageMaker Processing will reduce the cost of the data preprocessing job, since the ml.r5 instance is optimized for memory-intensive workloads and has a lower cost per GB of memory than the ml.m5 instance. For example, an ml.r5.4xlarge instance has 128 GB of memory and costs $1.008 per hour, while an ml.m5.4xlarge instance has 64 GB of memory and costs $0.888 per hour1. Therefore, the ml.r5.4xlarge instance can process the same amount of data in half the time and at a lower cost than the ml.m5.4xlarge instance. Moreover, using Amazon SageMaker Processing will allow the data preprocessing job to run on a separate, fully managed infrastructure that can be scaled up or down as needed, without affecting the notebook instance.

The other options are not as effective as option C for the following reasons:

Option A is not optimal because changing the notebook instance type to a memory optimized instance with the same vCPU number as the ml.m5.4xlarge instance has will not reduce the cost of the notebook, since the memory optimized instances have a higher cost per vCPU than the general-purpose instances. For example, an ml.r5.4xlarge instance has 16 vCPUs and costs $1.008 per hour, while an ml.m5.4xlarge instance has 16 vCPUs and costs $0.888 per hour1. Moreover, running both data preprocessing and feature engineering development on the same instance will not take advantage of the scalability and flexibility of Amazon SageMaker Processing.

Option B is not suitable because running data preprocessing on a P3 instance type with the same memory as the ml.m5.4xlarge instance by using Amazon SageMaker Processing will not reduce the cost of the data preprocessing job, since the P3 instance type is optimized for GPU-based workloads and has a higher cost per GB of memory than the ml.m5 or ml.r5 instance types. For example, an ml.p3.2xlarge instance has 61 GB of memory and costs $3.06 per hour, while an ml.m5.4xlarge instance has 64 GB of memory and costs $0.888 per hour1. Moreover, the data preprocessing job does not require GPU, so using a P3 instance type will be wasteful and inefficient.

Option D is not feasible because running data preprocessing on an R5 instance with the same memory size as the ml.m5.4xlarge instance by using the Reserved Instance option will not reduce the cost of the data preprocessing job, since the Reserved Instance option requires a commitment to a consistent amount of usage for a period of 1 or 3 years3. However, the data preprocessing job only runs once a day on average and completes in only 2 hours, so it does not have a consistent or predictable usage pattern. Therefore, using the Reserved Instance option will not provide any cost savings and may incur additional charges for unused capacity.

Amazon SageMaker Pricing

Manage Notebook Instances - Amazon SageMaker

Amazon EC2 Pricing - Reserved Instances