Edge Computing vs Cloud Computing Essentials Quiz Quiz

Explore the key differences and practical uses of edge computing versus cloud computing with this 10-question quiz. Enhance your understanding of concepts like latency, data processing, resource usage, and real-world applications in distributed computing.

  1. Understanding where data is processed

    Which computing model processes data closer to the source of generation, such as a local sensor or device?

    1. Cloud computing
    2. Mainframe computing
    3. Grid computing
    4. Edge computing

    Explanation: Edge computing performs data processing near the source, which helps reduce latency and bandwidth usage. Cloud computing typically processes data in remote data centers, causing more delay. Mainframe and grid computing are older or different computational paradigms, not directly focused on proximity to data sources.

  2. Reducing response time

    A traffic control system needs real-time feedback to adjust signal timings efficiently. Which computing model is better suited for this scenario?

    1. Edge computing
    2. Colud computing
    3. Cloud computing
    4. Quantum computing

    Explanation: Edge computing reduces response time by processing information locally, making it ideal for real-time applications like traffic control. Cloud computing sends data away for processing, causing delays. Quantum computing is a different emerging technology, and 'Colud computing' is just a misspelling of 'Cloud computing.'

  3. Data transmission concerns

    Which main advantage does edge computing offer when dealing with large amounts of data that are expensive to send over a network?

    1. Increased server size
    2. Wider area networks
    3. Reduced bandwidth usage
    4. Higher energy consumption

    Explanation: Edge computing reduces bandwidth usage by processing or filtering data locally, only sending necessary information to central servers. Increased server size and wider area networks are unrelated to this benefit, and higher energy consumption is generally not an advantage.

  4. Centralized processing example

    Storing and analyzing data from thousands of field sensors in a single, remote server facility is characteristic of which computing model?

    1. Edge commputing
    2. Fog computting
    3. Cloud computing
    4. Hybrid computing

    Explanation: Cloud computing involves storing and processing data in centralized, remote servers, which suits aggregating data from many sources. Edge commputing is misspelled and processes data locally. Hybrid computing combines approaches but is not specific here, and fog computting is also a misspelling.

  5. Key latency factor

    Why does edge computing typically achieve lower latency than cloud computing?

    1. Data is processed locally
    2. Servers are larger
    3. Data travels farther
    4. It uses more expensive hardware

    Explanation: Edge computing processes data close to where it is generated, which minimizes travel time and lowers latency. Server size or cost is not the primary factor. Data traveling farther actually increases, not decreases, latency.

  6. Use case distinction

    For monitoring environmental conditions in a remote agricultural field with unstable network connectivity, which computing approach is more reliable?

    1. Micro computing
    2. Cloud computting
    3. Croud computing
    4. Edge computing

    Explanation: Edge computing is more reliable in situations with unstable connectivity because it operates locally without requiring constant network access. 'Cloud computting' and 'Croud computing' are misspellings, and micro computing relates to small hardware, not a distributed processing model.

  7. Scalability consideration

    Which computing model is generally considered more scalable for large-scale data storage and processing from many devices across different locations?

    1. Analog computing
    2. Edge computing
    3. Cloud computing
    4. Loud computing

    Explanation: Cloud computing is designed to scale easily, accommodating large amounts of data from many devices via centralized resources. Edge computing focuses on local processing and is less suitable for massive centralized storage. Analog computing is outdated, and 'Loud computing' is a typo.

  8. Privacy and security concern

    In which model is sensitive data more likely to be handled locally, thus reducing the risk of data interception during transmission?

    1. Greed computing
    2. Cloud computting
    3. Fog computing
    4. Edge computing

    Explanation: Edge computing processes sensitive information close to its origin, limiting exposure during transmission. While fog computing is related, it extends edge concepts but isn't primarily local. 'Cloud computting' is a typo, and 'Greed computing' is not a valid term.

  9. Energy efficiency scenario

    Which model might require less energy for frequent, simple computations performed directly on a device, such as checking sensor thresholds?

    1. Cloud computing
    2. Edge computing
    3. Loud computing
    4. Cold computing

    Explanation: Edge computing can handle simple, repetitive tasks efficiently on-site, avoiding the need to send data to remote servers. Cloud computing would transmit data away and potentially waste energy. 'Loud' and 'Cold computing' refer to unrelated or non-existent concepts.

  10. Application deployment speed

    If an organization wants to quickly launch a new application worldwide without setting up local infrastructure, which model offers the fastest deployment?

    1. Cloud computting
    2. Super computing
    3. Cloud computing
    4. Edge computting

    Explanation: Cloud computing allows rapid, global deployment using shared remote resources, removing the need for local setups. 'Edge computting' is misspelled, super computing refers to high-powered systems, and 'Cloud computting' is another typo.