Vce MLS-C01 Questions Latest

 Binary classification is a type of supervised learning problem where the goal is to predict a categorical label that has only two possible values, such as Yes or No, True or False, Positive or Negative. In this case, the label is whether a transaction is fraudulent or not, which is a binary outcome. Binary classification can be used to estimate the probability of an observation belonging to a certain class, such as the probability of a transaction being fraudulent. This can help the business to make decisions based on the risk level of each transaction. References:

Binary Classification - Amazon Machine Learning

AWS Certified Machine Learning - Specialty Sample Questions

 To use a custom algorithm in Amazon SageMaker, the Docker container image must have an executable file named train that acts as the ENTRYPOINT for the container. This file is responsible for running the training code and communicating with the Amazon SageMaker service. The train file must be in the PATH of the container and have execute permissions. The other options are not valid ways to package the Docker container for Amazon SageMaker. References:

Use Docker containers to build models - Amazon SageMaker

Create a container with your own algorithms and models - Amazon SageMaker

 AWS KMS is a service that provides encryption and key management for data stored in AWS services and applications. AWS KMS can generate and manage encryption keys that are used to encrypt and decrypt data at rest and in transit. AWS KMS can also integrate with other AWS services, such as Amazon S3 and Amazon SageMaker, to enable encryption of data using the keys stored in AWS KMS. Amazon S3 is a service that provides object storage for data in the cloud. Amazon S3 can use AWS KMS to encrypt data at rest using server-side encryption with AWS KMS-managed keys (SSE-KMS). Amazon SageMaker is a service that provides a platform for building, training, and deploying machine learning models. Amazon SageMaker can use AWS KMS to encrypt data at rest on the SageMaker instances and volumes, as well as data in transit between SageMaker and other AWS services. AWS Glue is a service that provides a serverless data integration platform for data preparation and transformation. AWS Glue can use AWS KMS to encrypt data at rest on the Glue Data Catalog and Glue ETL jobs. AWS Glue can also use built-in or custom classifiers to identify and redact sensitive data, such as credit card numbers, from the customer data1234

The other options are not valid or secure ways to encrypt the data and protect the credit card information. Using a custom encryption algorithm to encrypt the data and store the data on an Amazon SageMaker instance in a VPC is not a good practice, as custom encryption algorithms are not recommended for security and may have flaws or vulnerabilities. Using the SageMaker DeepAR algorithm to randomize the credit card numbers is not a good practice, as DeepAR is a forecasting algorithm that is not designed for data anonymization or encryption. Using an IAM policy to encrypt the data on the Amazon S3 bucket and Amazon Kinesis to automatically discard credit card numbers and insert fake credit card numbers is not a good practice, as IAM policies are not meant for data encryption, but for access control and authorization. Amazon Kinesis is a service that provides real-time data streaming and processing, but it does not have the capability to automatically discard or insert data values. Using an Amazon SageMaker launch configuration to encrypt the data once it is copied to the SageMaker instance in a VPC is not a good practice, as launch configurations are not meant for data encryption, but for specifying the instance type, security group, and user data for the SageMaker instance. Using the SageMaker principal component analysis (PCA) algorithm to reduce the length of the credit card numbers is not a good practice, as PCA is a dimensionality reduction algorithm that is not designed for data anonymization or encryption.

Image classification is a machine learning task that involves assigning labels to images based on their content. Image classification can be performed using various algorithms, such as convolutional neural networks (CNNs), which are a type of deep learning model that can learn to extract high-level features from images. To train a customized image classification model, the e-commerce company needs a compute choice that can support the high computational demands of deep learning and provide good accuracy on the model quickly. A GPU accelerated computing instance, such as p3.2xlarge, is a suitable choice for this task, as it can leverage the parallel processing power of GPUs to speed up the training process and reduce the training time. A p3.2xlarge instance has one NVIDIA Tesla V100 GPU, which can provide up to 125 teraflops of mixed-precision performance and 16 GB of GPU memory. A p3.2xlarge instance can also use various deep learning frameworks, such as TensorFlow, PyTorch, MXNet, etc., to build and train the image classification model. A p3.2xlarge instance is also more cost-effective than a p3.8xlarge instance, which has four NVIDIA Tesla V100 GPUs, as the latter may not be necessary for a proof of concept with a small dataset. Therefore, the Machine Learning Specialist should select p3.2xlarge as the compute choice to train and achieve good accuracy on the model quickly.

References:

Amazon EC2 P3 Instances - Amazon Web Services

Image Classification - Amazon SageMaker

Convolutional Neural Networks - Amazon SageMaker

Deep Learning AMIs - Amazon Web Services