18 Questions
Scalability is a characteristic of Cloud computing.
True
Private Deployment Model is managed by the provider.
False
Machine Learning is a subfield of Artificial Intelligence.
True
Reinforcement Learning is a type of Supervised Learning.
False
Deep Learning is a subfield of Machine Learning.
True
SLA stands for Service Level Agreements.
True
Unsupervised Learning is used for Classification tasks.
False
Machine Learning is widely used in gaming, robotics, and industrial automation.
True
In Cloud computing, the Pay-per-usage model is a benefit of scalability.
False
Machine Learning can only be applied to data that is already labeled.
False
Deep Learning is a type of Reinforcement Learning.
False
In Cloud computing, the Public Deployment Model is managed by the provider.
False
Artificial Intelligence is a subfield of Machine Learning.
False
In Machine Learning, Supervised Learning is used for Clustering tasks.
False
Cloud computing provides Flexible resource allocation through self-service on demand.
True
Machine Learning is a type of Artificial Intelligence that can mimic human behavior.
True
In Cloud computing, the Hybrid Deployment Model combines Public and Private clouds.
True
Unsupervised Learning is used for Regression tasks.
False
Study Notes
Cloud Computing
- Flexible self-service, network-accessible computing resource pools that can be allocated to meet demand.
- Service models: IaaS, PaaS, SaaS.
- Deployment models: Private, Public, Hybrid.
- Benefits:
- Scalability
- Storage
- Security
- Data-loss prevention
- Maintenance
- Pay-per-usage of resources
- Accessibility
- Managed by the provider
- Flexible resource assignment
- Characteristics:
- Network accessible
- Sustainable
- Managed through self-service on demand
- SLA: Service Level Agreements
Machine Learning
- Definition: Process used to achieve artificial intelligence, involving designing algorithms that can learn from data to become more accurate and effective over time.
- Categories:
- Supervised learning
- Unsupervised learning
- Reinforcement learning
- Supervision:
- Classification: yes or no
- Regression: continuous
- Clustering: un-supervised
- Reinforcement learning:
- Training itself using trial and error
- Models learn from external interactions and improve with time
- Applications:
- Widely used in gaming, robotics, and industrial automation
- Healthcare and online stock trading
- Deep learning is a subfield of machine learning, inspired by how the brain works
Cloud Computing
- Flexible self-service, network-accessible computing resource pools that can be allocated to meet demand.
- Service models: IaaS, PaaS, SaaS.
- Deployment models: Private, Public, Hybrid.
- Benefits:
- Scalability
- Storage
- Security
- Data-loss prevention
- Maintenance
- Pay-per-usage of resources
- Accessibility
- Managed by the provider
- Flexible resource assignment
- Characteristics:
- Network accessible
- Sustainable
- Managed through self-service on demand
- SLA: Service Level Agreements
Machine Learning
- Definition: Process used to achieve artificial intelligence, involving designing algorithms that can learn from data to become more accurate and effective over time.
- Categories:
- Supervised learning
- Unsupervised learning
- Reinforcement learning
- Supervision:
- Classification: yes or no
- Regression: continuous
- Clustering: un-supervised
- Reinforcement learning:
- Training itself using trial and error
- Models learn from external interactions and improve with time
- Applications:
- Widely used in gaming, robotics, and industrial automation
- Healthcare and online stock trading
- Deep learning is a subfield of machine learning, inspired by how the brain works
Learn the basics of cloud computing including deployment models, service models, benefits, and characteristics. Understand the differences between IaaS, PaaS, and SaaS, and explore the advantages of cloud computing.
Make Your Own Quizzes and Flashcards
Convert your notes into interactive study material.
Get started for free
