AWS Certified AI Practitioner AIF-C01 Practice Question
Within artificial intelligence terminology, which statement most accurately describes what deep learning is and how it works?
It combines many decision trees into an ensemble to boost predictive accuracy.
It groups unlabeled data into clusters based on statistical similarity measures.
It trains an agent by rewarding actions that maximize cumulative returns within an environment.
It uses multi-layer neural networks that automatically learn hierarchical features from large datasets, reducing the need for manual feature engineering.
Deep learning is a subset of machine learning that relies on multi-layer ("deep") neural networks. These networks automatically discover useful hierarchical features from raw data during training, so manual feature engineering is minimized. Clustering algorithms, ensemble decision trees, and reinforcement learning techniques describe other machine-learning approaches, not deep learning.
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What are neural networks in deep learning?
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What is manual feature engineering, and why is it minimized in deep learning?
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How do multi-layer neural networks learn hierarchical features?
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What is the difference between deep learning and machine learning?
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What are hierarchical features in deep learning?
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What type of datasets work best with deep learning?
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AWS Certified AI Practitioner AIF-C01
Fundamentals of AI and ML
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