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Practice Questions
Test your understanding with targeted questions related to the topic.
Question 1
Easy
What is a container in the context of machine learning?
💡 Hint: Think about what is required to execute a machine learning model.
Question 2
Easy
Name a popular tool used for containerization.
💡 Hint: This tool is well-known for its ability to package applications.
Practice 4 more questions and get performance evaluation
Interactive Quizzes
Engage in quick quizzes to reinforce what you've learned and check your comprehension.
Question 1
What is the primary purpose of Docker?
- To deploy models
- To create containers
- To orchestrate applications
💡 Hint: Think about what Docker mainly focuses on.
Question 2
True or False: Kubernetes can automatically scale containerized applications based on user demand.
- True
- False
💡 Hint: Consider automation features of orchestration tools.
Solve 1 more question and get performance evaluation
Challenge Problems
Push your limits with challenges.
Question 1
Imagine you are tasked with deploying a new machine learning model using Docker and Kubernetes. Describe the steps you would follow to ensure a successful deployment.
💡 Hint: Consider every step from building to monitoring the deployment.
Question 2
You notice that the performance of a deployed ML model is degrading. How would you leverage Docker and Kubernetes to maintain its reliability?
💡 Hint: Think about how version control and rollback strategies might work in this context.
Challenge and get performance evaluation