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NEW QUESTION # 27
Deep Learning is a subset of ____.
Answer: C
Explanation:
The correct answer is A. Machine Learning . Deep learning is a specialized subset of machine learning that uses artificial neural networks with multiple layers to learn patterns from data. These layered neural networks can automatically discover features and representations from large datasets, which makes deep learning especially useful for image recognition, speech recognition, natural language processing, recommendation systems, and generative AI applications.
Artificial intelligence is the broader field that includes machine learning, expert systems, reasoning systems, robotics, natural language processing, and other intelligent technologies. Machine learning is a branch within artificial intelligence, and deep learning is a further subset within machine learning. Artificial Narrow Intelligence refers to AI systems designed for specific tasks, while Artificial General Intelligence refers to a theoretical system with broad human-like intelligence. Since deep learning is most directly and correctly classified as a subset of machine learning, the best answer is A .
NEW QUESTION # 28
If humans are labeling the data and the machine is correctly labeling current or future data points, it's ______.
Answer: A
Explanation:
The correct answer is A. supervised learning because supervised learning uses labeled data to train a machine learning model. In this method, humans or existing systems provide correct labels for the training examples, and the model learns the relationship between input data and the expected output labels. After training, the machine can apply what it has learned to correctly classify or label current and future data points.
Unsupervised learning is incorrect because it works with unlabeled data and discovers hidden patterns, groups, or structures without human-provided labels. Reinforcement learning is also incorrect because it is based on actions, rewards, penalties, and learning through interaction with an environment. Semi-supervised learning uses a combination of a small amount of labeled data and a larger amount of unlabeled data, but the question clearly states that humans are labeling the data. "Semi Reinforcement learning" is not the standard answer here. Therefore, the correct choice is A. supervised learning .
NEW QUESTION # 29
If humans are unlabeling the data and the machine is correctly labeling current or future data points, it's
______.
Answer: C
Explanation:
Semi-supervised learning is the correct answer because it combines a small amount of labeled data with a larger amount of unlabeled data. In this scenario, humans are not fully labeling the data, but the machine is still able to correctly label current or future data points by learning patterns from the available data. That matches the concept of semi-supervised learning, where the model uses limited human-provided labels and extends learning to unlabeled examples.
Supervised learning is not the best answer because supervised learning depends on clearly labeled training data supplied by humans. Unsupervised learning is also incorrect because it identifies hidden patterns or clusters without using labels, rather than predicting correct labels for future data points. Reinforcement learning is based on rewards, penalties, actions, and an environment, which is not described here. "Semi- reinforcement learning" is not a standard main category in machine learning.
Therefore, the most accurate answer is **E. Semi-supervised learning**.
NEW QUESTION # 30
What type of learning is used when a model is trained with labeled data?
Answer: A
Explanation:
The correct answer is B. Supervised Learning . Supervised learning is the machine learning approach used when a model is trained with labeled data. Labeled data means each training example includes both the input and the correct output or target label. The model studies these examples and learns the relationship between the input features and the expected result. After training, it can make predictions or classifications on new data.
Unsupervised learning is incorrect because it uses unlabeled data and focuses on finding hidden patterns, clusters, or structures without predefined answers. Reinforcement learning is incorrect because it involves an agent learning through actions, rewards, and penalties in an environment. Semi-supervised learning is also not the best answer because it uses a mix of labeled and unlabeled data. Support Vector refers to part of the Support Vector Machine method, not a learning type by itself. Therefore, the correct learning type for labeled data is B. Supervised Learning .
NEW QUESTION # 31
Choose the CORRECT example of Reinforcement Learning.
Answer: A
Explanation:
The correct answer is D. All of the above because robotics, game playing, and navigation are all common examples of reinforcement learning. Reinforcement learning is a machine learning approach in which an agent learns by interacting with an environment and receiving rewards or penalties based on its actions. Over time, the agent learns a policy that helps it maximize long-term reward.
Robotics is a strong example because robots can learn movement, object handling, path planning, and control actions through trial and feedback. Game playing is another classic reinforcement learning example because an AI agent can learn winning strategies by trying actions, observing outcomes, and improving decisions over repeated episodes. Navigation is also a valid example because an agent can learn the best route or movement strategy by receiving feedback about distance, obstacles, time, or success in reaching a goal.
Since all three listed options are valid applications of reinforcement learning, the correct answer is D. All of the above .
NEW QUESTION # 32
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