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AWS AI Practitioner (AIF-C01) · Free practice question 8 of 12

Deep learning learns features from raw data

Engineers at Fallow Vision explain why they chose deep learning instead of a traditional ML algorithm to recognize objects in raw photos. Which reason is most accurate?

  1. A.Deep learning models always produce fully interpretable decisions.
  2. B.Multi-layer neural networks can learn useful features directly from raw pixels instead of relying on hand-crafted features.
  3. C.Deep learning models need no training data.
  4. D.Deep learning works only with small, structured tabular datasets.
Show answer and explanation

Correct answer: B. Multi-layer neural networks can learn useful features directly from raw pixels instead of relying on hand-crafted features.

Why: Deep learning uses neural networks with many layers that learn increasingly abstract features, such as edges, shapes, and objects, directly from unstructured data like images, reducing the need for manual feature engineering. Deep learning typically needs large amounts of training data, is harder to interpret than simpler models, and is especially strong on unstructured data rather than limited to small tabular datasets.

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