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

 Quantile binning is a data transformation technique that can be used to handle skewed and non-linear numerical features. It divides the range of a feature into equal-sized bins based on the percentiles of the data. Each bin is assigned a numerical value that represents the midpoint of the bin. This way, the feature values are transformed into a more uniform distribution that can improve the performance of linear models. Quantile binning can also reduce the impact of outliers and noise in the data.

One-hot encoding, Cartesian product transformation, and normalization are not suitable for this scenario. One-hot encoding is used to transform categorical features into binary features. Cartesian product transformation is used to create new features by combining existing features. Normalization is used to scale numerical features to a standard range, but it does not change the shape of the distribution. References:

Data Transformations for Machine Learning

Quantile Binning Transformation

 To improve the training speed of a time-series forecasting model using TensorFlow, the Machine Learning Specialist should change the TensorFlow code to implement a Horovod distributed framework supported by Amazon SageMaker. Horovod is a free and open-source software framework for distributed deep learning training using TensorFlow, Keras, PyTorch, and Apache MXNet1. Horovod can scale up to hundreds of GPUs with upwards of 90% scaling efficiency2. Horovod is easy to use, as it requires only a few lines of Python code to modify an existing training script2. Horovod is also portable, as it runs the same for TensorFlow, Keras, PyTorch, and MXNet; on premise, in the cloud, and on Apache Spark2.

Amazon SageMaker is a fully managed service that provides every developer and data scientist with the ability to build, train, and deploy machine learning models quickly3. Amazon SageMaker supports Horovod as a built-in distributed training framework, which means that the Machine Learning Specialist does not need to install or configure Horovod separately4. Amazon SageMaker also provides a number of features and tools to simplify and optimize the distributed training process, such as automatic scaling, debugging, profiling, and monitoring4. By using Amazon SageMaker, the Machine Learning Specialist can parallelize the training to as many machines as needed to achieve the business goals, while minimizing coding effort and infrastructure changes.

References:

1: Horovod (machine learning) - Wikipedia

2: Home - Horovod

3: Amazon SageMaker – Machine Learning Service – AWS

4: Use Horovod with Amazon SageMaker - Amazon SageMaker

The solution C will meet the requirements with the least development effort because it uses Amazon Rekognition and AWS CloudTrail, which are fully managed services that can provide the desired functionality. The solution C involves the following steps:

Use Amazon Rekognition to identify celebrities in the pictures. Amazon Rekognition is a service that can analyze images and videos and extract insights such as faces, objects, scenes, emotions, and more. Amazon Rekognition also provides a feature called Celebrity Recognition, which can recognize thousands of celebrities across a number of categories, such as politics, sports, entertainment, and media. Amazon Rekognition can return the name, face, and confidence score of the recognized celebrities, as well as additional information such as URLs and biographies1.

Use AWS CloudTrail to capture IP address and timestamp details. AWS CloudTrail is a service that can record the API calls and events made by or on behalf of AWS accounts. AWS CloudTrail can provide information such as the source IP address, the user identity, the request parameters, and the response elements of the API calls. AWS CloudTrail can also deliver the event records to an Amazon S3 bucket or an Amazon CloudWatch Logs group for further analysis and auditing2.

The other options are not suitable because:

Option A: Using AWS Panorama to identify celebrities in the pictures and using AWS CloudTrail to capture IP address and timestamp details will not meet the requirements effectively. AWS Panorama is a service that can extend computer vision to the edge, where it can run inference on video streams from cameras and other devices. AWS Panorama is not designed for identifying celebrities in pictures, and it may not provide accurate or relevant results. Moreover, AWS Panorama requires the use of an AWS Panorama Appliance or a compatible device, which may incur additional costs and complexity3.

Option B: Using AWS Panorama to identify celebrities in the pictures and making calls to the AWS Panorama Device SDK to capture IP address and timestamp details will not meet the requirements effectively, for the same reasons as option A. Additionally, making calls to the AWS Panorama Device SDK will require more development effort than using AWS CloudTrail, as it will involve writing custom code and handling errors and exceptions4.

Option D: Using Amazon Rekognition to identify celebrities in the pictures and using the text detection feature to capture IP address and timestamp details will not meet the requirements effectively. The text detection feature of Amazon Rekognition is used to detect and recognize text in images and videos, such as street names, captions, product names, and license plates. It is not suitable for capturing IP address and timestamp details, as these are not part of the pictures that users upload. Moreover, the text detection feature may not be accurate or reliable, as it depends on the quality and clarity of the text in the images and videos5.

References:

1: Amazon Rekognition Celebrity Recognition

2: AWS CloudTrail Overview

3: AWS Panorama Overview

4: AWS Panorama Device SDK

5: Amazon Rekognition Text Detection