AWS Machine Learning Sample Questions is a great resource for those who are preparing for the AWS Machine Learning Certification Exam. The questions are divided into four sections: Data Preprocessing, Modeling, Tuning, and Evaluation.
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AWS Machine Learning – Introduction
AWS Machine Learning is a cloud-based service that makes it easy for developers to train and deploy machine learning models. With AWS Machine Learning, developers can quickly build and deploy predictive models that make predictions on new data.
AWS Machine Learning is based on the open-source machine learning library, Apache MXNet. Apache MXNet is a fast and scalable library for deep learning that enables developers to build and train machine learning models on a variety of devices, including processors, graphics processing units (GPUs), and field-programmable gate arrays (FPGAs).
AWS Machine Learning offers a number of benefits for developers, including the ability to:
– Train machine learning models on Amazon Elastic Compute Cloud (Amazon EC2) instances or Amazon SageMaker notebooks.
– Deploy trained models to Amazon SageMaker hosted endpoints or Amazon EC2 instances.
– Make predictions on new data using deployed models.
– Monitor the performance of deployed models using Amazon CloudWatch metrics and AWS CloudTrail logs.
AWS Machine Learning – Data Preparation
1. Why is it important to have label encoded data when working with machine learning algorithms?
2. What is the purpose of creating a validation set in data preparation for machine learning?
3. Why is it important to scale data before training a machine learning model?
4. How can you tell if your data is ready for modeling?
5. What are some techniques you can use to improve your machine learning model?
AWS Machine Learning – Data Modelling
1. What is a data model?
A data model is an abstract model that describes the structure of data, relationships between data elements, and semantics for data processing.
2. What is a supervised learning algorithm?
A supervised learning algorithm is an algorithm that is used to learn from training data to generate a model that can be used to make predictions on new, unseen data. Supervised learning algorithms are often used in classification and regression tasks.
3. What is a unsupervised learning algorithm?
A unsupervised learning algorithm is an algorithm that is used to learn from data without any labeled training data. Unsupervised learning algorithms are often used for tasks such as clustering and dimensionality reduction.
AWS Machine Learning – Evaluation
Below are five sample questions related to the AWS Machine Learning platform. These questions are designed to assess your knowledge of the platform and its capabilities.
1. What is AWS Machine Learning?
AWS Machine Learning is a cloud-based platform that enables developers to build, train, and deploy machine learning models. The platform provides access to a variety of machine learning algorithms, tools, and services, making it easy to get started with machine learning.
2. What are some of the features of AWS Machine Learning?
Some of the features of AWS Machine Learning include: data pre-processing, feature engineering, model training, model deployment, and model monitoring. The platform also provides access to a variety of machine learning algorithms, tools, and services.
3. How can I use AWS Machine Learning to build a machine learning model?
You can use AWS Machine Learning to build a machine learning model by following these steps: 1) collect and prepare your data; 2) choose an algorithm; 3) train your model; 4) deploy your model; and 5) monitor your model.
4. How do I deploy a machine learning model on AWS Machine Learning?
You can deploy a machine learning model on AWS Machine Learning by using the built-in deployment wizard. This wizard will guide you through the process of creating an Amazon SageMaker endpoint for your model. Once your endpoint is created, you can then use it to make predictions on new data.
AWS Machine Learning – Deployment
1. What are the two options for deploying a machine learning solution on Amazon Web Services (AWS)? (Choose two.)
A. Use Amazon Elastic Container Service (ECS) to deploy a Docker container that contains your machine learning code.
B. Use AWS Lambda to run your machine learning code on demand.
C. Use Amazon SageMaker to build and deploy a machine learning model.
D. Use Amazon Elastic Compute Cloud (EC2) to launch a virtual machine that runs your machine learning code.
2. You have built a binary classification model using Amazon SageMaker and now want to deploy it to an endpoint so that it can make predictions for new data. Which of the following is true about creating an endpoint? (Choose all that apply.)
A. You can deploy multiple models to the same endpoint by using different variants.
B. You must specify the type of instance that you want to use for hosting your endpoint when you create it.
C. You can update the configuration of an existing endpoint by redeploying a new model to it with different settings.
D. After you have deployed a model to an endpoint, you cannot redeploy a new version of the model to the endpoint unless you first delete the existing endpoint and create a new one.
3. Your data scientist has built a predictive maintenance model using Amazon SageMaker and now wants to deploy it so that it can start making predictions against new data streams coming from your production equipment sensors in real time, 24/7/365.Which of the following is true? (Choose all that apply.)
A .You deployed your predictive maintenance model as an Amazon SageMaker batch transform job which takes batches of historical sensor data as input and makes predictions for each batch..
B .You deployed your predictive maintenance model using Amazon API Gateway and AWS Lambda so that predictions are made only when invoked by an HTTPS API call, which could be automated or triggered manually as needed..
C .You deployed your predictive maintenance model directly on an edge device such as a Raspberry Pi3 so that predictions are made locally without incurring any latency from calling into AWS.. D .You deployedyour predictive maintenancemodel using AWS IoT Greengrassso that predictions are made locally on the edge device and, if needed, invocation requests can be routed automaticallyto other devices or backends for further processing..
4..What is one way to improve performance when making predictions with an Amazon SageMaker end? (Select 1 answer)A . Increase the number of records in each mini-batch prediction request..B . Reduce the number of records in each mini-batch prediction request..C . Cache frequently accessed data on the local storage volume attached host instance used for prediction..D . Configure Auto Scaling groupsfor both trainingand predictioninstances so that resourcescan be added or removedas neededto respond tonew workload patterns…
AWS Machine Learning – Sample Questions
1. What is Amazon Machine Learning?
Amazon Machine Learning is a service that makes it easy for developers of all skill levels to use machine learning technology. Amazon Machine Learning provides visualization tools and wizards that guide you through the process of creating machine learning models without having to learn complex algorithms or mathematics.
2. What are the benefits of using Amazon Machine Learning?
Amazon Machine Learning makes it easy to get started with machine learning, helps you gain insights from your data quickly, and enables you to deploy predictive models at scale. With Amazon Machine Learning, you can:
– Get started quickly by using wizards to create models with just a few clicks.
– Gain valuable insights from your data using visualizations that show you relationships and patterns that you might not have otherwise noticed.
– Deploy your models rapidly and at scale using Amazon’s world-class infrastructure and services such as Amazon S3, Amazon EC2, and Amazon API Gateway.
– Pay only for the resources you use with no upfront commitment or long-term contracts required.
3. How does Amazon Machine Learning work?
You begin by providing labeled training data (for example, a dataset with features such as age, location, salary, and whether or not someone purchased a particular product). Amazon Machine Learning uses this training data to train a predictive model. The model can then be used to make predictions on new data (for example, age, location, salary) to generate predictions (for example, purchase probability).
AWS Machine Learning – Further Reading
An in-depth guide to Amazon’s AWS Machine Learning platform, including frequently asked questions and further reading on the subject.
AWS Machine Learning – FAQs
1. What is Amazon ML?
Amazon ML is a cloud-based service that makes it easy for developers of all skill levels to use machine learning technology. Amazon ML provides visualization tools and wizards that guide you through the process of creating machine learning models without having to learn complex algorithms or programming languages. Amazon ML also makes it easy to evaluate the performance of your models and deploy them into production.
2. How does Amazon ML work?
Amazon ML uses a combination of advanced algorithms and techniques to train machine learning models. After you provide training data, Amazon ML automatically tunes model parameters to improve performance. Amazon ML also provides tools that help you determine whether your models are ready for deployment.
3. What are the benefits of using Amazon ML?
Amazon ML makes it easy to get started with machine learning, and can help you improve the accuracy of your models with little effort on your part. Additionally, Amazon ML provides tight integration with other AWS services, making it easy to deploy your machine learning models in production.
AWS Machine Learning – Glossary
AWS Machine Learning is a service that allows developers to build models to make predictions on data using Amazon’s built-in algorithms. The service can be used to train and deploy machine learning models.
Here are some key terms you should know when using AWS Machine Learning:
Algorithm: A set of instructions for training a machine learning model. Amazon provides a variety of algorithms that can be used with AWS Machine Learning.
Dataset: A collection of data that will be used to train a machine learning model. This data can be in the form of text, numerical values, or images.
Feature: A characteristic or property of a dataset that can be used to make predictions. For example, in a dataset of fruit, the features might be color, shape, and size.
Label: A value that is assigned to a data point in order to indicate what category it belongs to. For example, in a dataset of fruit, the labels might be apples, oranges, and bananas.
Prediction: A value that is generated by a machine learning model based on one or more features of a data point. For example, if you use a machine learning model to predict the price of a house based on its square footage, the prediction would be the estimated price of the house.
AWS Machine Learning – Resources
Below are some useful resources if you’re just getting started with AWS Machine Learning, or if you want to learn more about the platform.
-The AWS Machine Learning Blog is a great place to start if you want to learn more about the platform and what it can do.
-The Amazon Web Services Training and Certification website offers courses and certification exams for many of the services that make up the platform, including machine learning.
-If you’re looking for a more hands-on approach, check out the AWSMachine Learning Interior Node Bootcamp. This course will take you through the process of setting up and using an Amazon EC2 instance for machine learning tasks.
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