Machine Learning Data Fundamentals Quiz
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Why do data-related tasks take so much time and effort in typical machine learning projects?

  • Machine learning projects do not require extensive data-related tasks
  • Many tasks like data identification, aggregation, cleaning, labelling, and augmentation are specific to problem domains and require custom solutions (correct)
  • Data-related tasks involve simple and straightforward processes
  • Most tasks rely on automated processes which are time-consuming

    • What does the 'Velocity' aspect of the 4V's of Big Data refer to?

  • There is a lot of data to store, requiring a lot of storage
  • Data is of uncertain or dubious quality, requiring systems to cope with data uncertainty
  • Data comes in many different formats and forms
  • Data comes in at high rates and speeds, requiring the ability to ingest data at high rates (correct)

    • What is the main challenge posed by the 'Variety' aspect of Big Data?

  • There is a lot of data to store, requiring a lot of storage
  • Data comes in at high rates and speeds
  • Data is of uncertain or dubious quality
  • Data comes in many different formats and forms, requiring systems to be flexible (correct)

    • How can the 4V's of Big Data be addressed by current technology?

    <p>Technology can measure and cope with data uncertainty and quality</p> Signup and view all the answers

    What contributes to the time and effort required for data-related tasks in machine learning projects?

    <p>Many tasks are specific to problem domains and require custom solutions</p> Signup and view all the answers

    What are some specific data-related tasks that contribute to the time and effort in typical machine learning projects?

    <p>Data identification, data aggregation, data cleaning, data labeling, and data augmentation.</p> Signup and view all the answers

    Why do most data-related tasks in machine learning projects require custom-made solutions?

    <p>Most tasks are specific to a given problem domain and cannot be solved in a general fashion, thus requiring custom-made solutions.</p> Signup and view all the answers

    Briefly summarize the 4V's of Big Data.

    <p>Volume: Refers to the size of data and the need for a lot of storage. Velocity: Involves the high rate at which data comes in and the need to ingest data at high rates. Variety: Refers to data coming in many formats and the need for system flexibility. Veracity: Involves uncertain or dubious quality of data and the need to measure and cope with data uncertainty.</p> Signup and view all the answers

    What is the significance of the 'Velocity' aspect of the 4V's of Big Data?

    <p>It involves the high rate at which data comes in and the need to ingest data at high rates.</p> Signup and view all the answers

    How can the 4V's of Big Data be addressed by current technology?

    <p>This question is not explicitly addressed in the provided text.</p> Signup and view all the answers

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