Cloud Machine Learning Engineering and MLOps Quiz Answers

Get All Weeks Cloud Machine Learning Engineering and MLOps Quiz Answers

Week 01: Cloud Machine Learning Engineering and MLOps Quiz Answers

Quiz 01: Week 1 Quiz

Q1. What is a key difference between Data Science and ML Engineering?

  • Models go to production in ML Engineering
  • Model accuracy is most important for ML Engineering
  • Models should be share on Kaggle in ML Engineering

Q2. Why is an advantage of using a widely used ML Platform?

  • Maintainability
  • Easy to hire talent
  • Communication

Q3. What is an advantage of Flask for ML Engineering?

  • Easy to create Microservices
  • Has an admin interface
  • Designed for building a Content Management Site

Q4. How can ML Engineering used?

  • Building mobile apps
  • Create working systems that deliver predictions
  • Building web apps

Q5. What is Continuous Delivery?

  • Code is always in a deployable state
  • It is a database system
  • It is an algorithm

Q6. What would be an example of an ML application?

  • Automated License plate reader
  • Mobile Photo Sharing app
  • Blog

Q7. Why would a Microservice be valuable for ML?

  • Single purpose
  • The Microservice can turn into a mobile app
  • It can make websites

Q8. What is an example of a Machine Learning Engineering platform?

  • Google News
  • AWS Sagemaker
  • Google Analytics

Q9. What problems do Machine Learning platforms solve?

  • Object Storage
  • Training large models
  • Block Storage

Q10. What advantage could a ML platform create for deployment?

  • Create a new job, release manager
  • Adds more human QA
  • Deployment to scalable endpoints

Week 02: Cloud Machine Learning Engineering and MLOps Quiz Answers

Quiz : Week 2 Quiz

Q1. What is AutoML?

  • A form of Machine Learning training that is fully automated
  • A web service
  • An API

Q2. What type of problem could you solve with Cloud AutoML?

  • Websites
  • AGI (Artificial General Intelligence)
  • Computer Vision

Q3. Why would an organization want to use AutoML vs tuning Hyperparameters themselves?

  • Better accuracy
  • Increase the velocity of model deployment
  • This is rarely done because humans must modify Hyperparemeters

Q4. What is Ludwig?

  • A closed course AutoML system
  • A toolbox for creating ML models without code
  • An AutoML system that requires deep software skills

Q5. What is an advantage of AutoML?

  • Human judgement to evaluate conclusion is removed
  • Bad data is automatically fixed
  • Train many models at the same time

Q6. How could AutoML help explainability of a model?

  • They come with a staff of experts
  • Accuracy is improved through complexity
  • Automated Explainability tools

Q7. Where is a popular location designed to download pre-trained models?

  • Tensorflow Hub
  • Github
  • Bitbucket

Q8. Which are examples of AutoML systems?

  • Google Cloud AutoML Vision
  • Azure ML Studio
  • AWS Sagemaker AutoPilot

Q9. What is an example of a ML model deployment target for AutoML?

  • Edge Device
  • Mobile
  • Database

Q10. What is an example of an AutoML solution by Apple?

  • Create ML
  • iOS
  • OS X

Week 03: Cloud Machine Learning Engineering and MLOps Quiz Answers

Quiz : Week 3 Quiz

Q1. What is MLOps?

  • Testing
  • QA
  • Combination of best practices of DevOps and Machine Learning

Q2. What advantage does an AI API offer?

  • Free
  • Leverage the expertise of experts
  • Custom business logic

Q3. What is a use case for Edge ML?

  • Desktop PC
  • Low latency prediction
  • Windows Server

Q4. What is an advantage of small edge inference?

  • Includes AutoML
  • Doing ML predictions on portable devices
  • More powerful than GPU

Q5. What is a sentiment analysis API?

  • A feature in a blog
  • A feature in a mobile app
  • Detects the emotion in text

Q6. What is an advantage of medical AI APIs?

  • Free
  • They only run on Mobile
  • Can validate that correct prescription drugs are given

Q7. Why would a company shift resources from Data Science to MLOps

  • They cannot hire Data Scientists
  • Increase the models that make it to production
  • They don’t care about model quality

Q8. What is one thing MLOps does?

  • Enables Data Science and IT to work together
  • Builds websites
  • Builds mobile apps

Q9. Why would a company care about “Data Drift”?

  • Data drift makes models more explainable
  • Data Drift is helpful to model accuracy
  • Model accuracy

Q10. Why would an MLOPs practitioner need to know Continuous Integration?

  • The foundation of MLOps
  • It is a classification algorithm
  • It is a regression algorithm

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