AI0-001 AI Concepts and Techniques Practice Question

Answer analysis

Option-by-option breakdown

For each option: why learners choose it and why it is or isn't the right answer here.

• ✓

  Narrow AI is designed for a specific task; general AI aims to perform any cognitive task a human can.

  Why this is correct

  Narrow AI is scoped to one specific task, such as image classification or recommendation, and cannot transfer that capability elsewhere. General AI denotes a hypothetical system able to perform any cognitive task a human can, matching the stem's task-specificity versus broad-capability distinction.

• ✗

  Narrow AI relies on supervised learning; general AI uses unsupervised learning exclusively.

  Why it's wrong here

  The distinction concerns task scope, not learning paradigm; narrow AI can use unsupervised or reinforcement learning, and hypothetical general AI would likely combine several. The supervised/unsupervised split is tempting because most deployed narrow models are trained supervised, but that correlation is not the definition.

• ✗

  Narrow AI requires large datasets; general AI can learn from few examples.

  Why it's wrong here

  Narrow and general AI differ by breadth of cognitive capability, not training-data volume; narrow AI can also learn from few examples via transfer learning. Data-hunger is tempting because today's narrow systems often need large datasets, but that is an implementation trait, not the defining axis.

• ✗

  Narrow AI can perform any intellectual task; general AI is limited to specific tasks.

  Why it's wrong here

  This reverses the definitions: narrow AI is confined to specific tasks, while general AI would handle any intellectual task a human can. The reversal is tempting because both terms sound like capability labels, but the stem asks for the actual difference, which this option inverts.