Amazon Web Services MLS-C01 Questions Answers

The best storage option for the trucking company is to use an Amazon S3-backed data lake to store the raw images, and set up the permissions using bucket policies. A data lake is a centralized repository that allows you to store all your structured and unstructured data at any scale. Amazon S3 is the ideal choice for building a data lake because it offers high durability, scalability, availability, and security. You can store any type of data in Amazon S3, such as images, videos, audio, text, etc. You can also use AWS services such as Amazon Rekognition, Amazon SageMaker, and Amazon EMR to analyze and process the data in the data lake. To ensure the data is only accessible to specific IAM users, you can use bucket policies to grant or deny access to the S3 buckets based on the IAM user’s identity or role. Bucket policies are JSON documents that specify the permissions for the bucket and the objects in it. You can use conditions to restrict access based on various factors, such as IP address, time, source, etc. By using bucket policies, you can control who can access the data in the data lake and what actions they can perform on it.

AWS Machine Learning Specialty Exam Guide

AWS Machine Learning Training - Build a Data Lake Foundation with Amazon S3

AWS Machine Learning Training - Using Bucket Policies and User Policies

The best architecture to scale the solution at the lowest cost is to implement the solution using AWS Deep Learning Containers and run the container as a job using AWS Batch on a GPU-compatible Spot Instance. This option has the following advantages:

AWS Deep Learning Containers: These are Docker images that are pre-installed and optimized with popular deep learning frameworks such as TensorFlow, PyTorch, and MXNet. They can be easily deployed on Amazon EC2, Amazon ECS, Amazon EKS, and AWS Fargate. They can also be integrated with AWS Batch to run containerized batch jobs. Using AWS Deep Learning Containers can simplify the setup and configuration of the deep learning environment and reduce the complexity of the resource management.

AWS Batch: This is a fully managed service that enables you to run batch computing workloads on AWS. You can define compute environments, job queues, and job definitions to run your batch jobs. You can also use AWS Batch to automatically provision compute resources based on the requirements of the batch jobs. You can specify the type and quantity of the compute resources, such as GPU instances, and the maximum price you are willing to pay for them. You can also use AWS Batch to monitor the status and progress of your batch jobs and handle any failures or interruptions.

GPU-compatible Spot Instance: This is an Amazon EC2 instance that uses a spare compute capacity that is available at a lower price than the On-Demand price. You can use Spot Instances to run your deep learning training jobs at a lower cost, as long as you are flexible about when your instances run and how long they run. You can also use Spot Instances with AWS Batch to automatically launch and terminate instances based on the availability and price of the Spot capacity. You can also use Spot Instances with Amazon EBS volumes to store your datasets, checkpoints, and logs, and attach them to your instances when they are launched. This way, you can preserve your data and resume your training even if your instances are interrupted.

AWS Deep Learning Containers

AWS Batch

Amazon EC2 Spot Instances

Using Amazon EBS Volumes with Amazon EC2 Spot Instances

In cases where survey questions allow multiple choices per question, one-hot encoding is an effective way to represent responses as binary features. Each possible option for each question is transformed into a separate binary column (1 if selected, 0 if not), providing a comprehensive and machine-readable format that logistic regression models can interpret effectively.

This approach ensures that each respondent’s selections are accurately captured in a format suitable for training, offering a straightforward representation for multi-choice responses.

 To find the most discussed topics in the comments that are in either English or Spanish, the machine learning specialist needs to perform two steps: first, translate the comments from Spanish to English if necessary, and second, apply a topic modeling algorithm to the comments. The following options are valid ways to accomplish these steps using AWS services:

Option C: Use Amazon Translate to translate from Spanish to English, if necessary. Use Amazon Comprehend topic modeling to find the topics. Amazon Translate is a neural machine translation service that delivers fast, high-quality, and affordable language translation. Amazon Comprehend is a natural language processing (NLP) service that uses machine learning to find insights and relationships in text. Amazon Comprehend topic modeling is a feature that automatically organizes a collection of text documents into topics that contain commonly used words and phrases.

Option E: Use Amazon Translate to translate from Spanish to English, if necessary. Use Amazon SageMaker Neural Topic Model (NTM) to find the topics. Amazon SageMaker is a fully managed service that provides every developer and data scientist with the ability to build, train, and deploy machine learning (ML) models quickly. Amazon SageMaker Neural Topic Model (NTM) is an unsupervised learning algorithm that is used to organize a corpus of documents into topics that contain word groupings based on their statistical distribution.

The other options are not valid because:

Option A: Amazon SageMaker BlazingText algorithm is not a topic modeling algorithm, but a text classification and word embedding algorithm. It cannot find the topics independently from language, as different languages have different word distributions and semantics.

Option B: Amazon SageMaker seq2seq algorithm is not a translation algorithm, but a sequence-to-sequence learning algorithm that can be used for tasks such as summarization, chatbot, and question answering. Amazon SageMaker Latent Dirichlet Allocation (LDA) algorithm is a topic modeling algorithm, but it requires the input documents to be in the same language and preprocessed into a bag-of-words format.

Option D: Amazon Lex is not a topic modeling algorithm, but a service for building conversational interfaces into any application using voice and text. It cannot extract topics from the content, but only intents and slots based on a predefined bot configuration. References:

Amazon Translate

Amazon Comprehend

Amazon SageMaker

Amazon SageMaker Neural Topic Model (NTM) Algorithm

Amazon SageMaker BlazingText

Amazon SageMaker Seq2Seq

Amazon SageMaker Latent Dirichlet Allocation (LDA) Algorithm

Amazon Lex