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質問 # 38
What type of AI system does not have self-awareness or consciousness?
正解：B
解説：
The correct answer is C. Narrow AI . Narrow AI, also called Weak AI, is designed to perform specific tasks within a limited domain. Examples include recommendation systems, chatbots, image recognition tools, fraud detection systems, voice assistants, and predictive analytics models. These systems can appear intelligent because they process data, detect patterns, make predictions, or generate responses, but they do not possess self-awareness, consciousness, emotions, independent understanding, or human-like general reasoning.
General AI and Strong AI refer to the idea of an AI system that could reason, learn, and adapt across many different tasks in a human-like way. These terms are associated with broader intelligence rather than task- specific automation. Human AI is not a standard AI category in this context. Since the question asks for the type of AI that does not have self-awareness or consciousness and operates only within defined limits, the correct choice is C. Narrow AI .
質問 # 39
Choose the CORRECT reasons. We want to study AI to automate things, because
正解：A
解説：
The correct answer is E. All of the above because each statement gives a valid reason for studying and using AI to automate tasks. Modern organizations deal with massive volumes of data that are too large and complex for humans to process manually. AI helps analyze this data quickly, detect patterns, and support better decisions.
Statement B is also correct because data now comes from many sources at the same time, including sensors, applications, customers, transactions, machines, documents, and digital platforms. This data is often unstructured, noisy, and difficult to manage without intelligent automation. Statement C is correct because business knowledge must be updated continuously as data changes. AI systems can learn from new patterns and support faster adaptation. Statement D is also correct because many AI applications, such as robotics, autonomous systems, fraud detection, and industrial automation, require real-time sensing, decision-making, and precise action.
Since all four reasons support the need for AI-driven automation, the correct answer is E. All of the above .
質問 # 40
Which of the following is NOT CORRECT for the Elbow method?
正解：D
解説：
The correct answer is E. None of the above because all three statements about the Elbow method are correct.
The Elbow method is commonly used in unsupervised learning, especially with K-means clustering, to help estimate an appropriate number of clusters. It works by running clustering with different values of K and measuring the within-cluster variation or distortion. As K increases, the error usually decreases, but after a certain point the improvement becomes much smaller. That point is visually interpreted as the "elbow." Statement A is correct because the Elbow method helps determine how many clusters should be formed.
Statement B is also correct because it is widely used with K-means clustering to select a suitable value of K.
Statement C is correct because the method is a heuristic, meaning it is a practical estimation technique rather than an exact mathematical guarantee. Since A, B, and C are all correct, none of them is NOT correct.
Therefore, the correct answer is E. None of the above .
質問 # 41
An AI agent learns to play a game by taking actions, receiving rewards for good moves, and penalties for poor moves. Over time, it improves its strategy to maximize total reward. This is an example of ______.
正解：E
解説：
Reinforcement learning is the correct answer because the AI agent learns by interacting with an environment and improving its behavior based on rewards and penalties. The goal of reinforcement learning is to learn a policy or strategy that maximizes cumulative reward over time. This differs from supervised learning, where the model learns from labeled input-output examples. It also differs from unsupervised learning, where the model searches for hidden patterns without labels or rewards. Semi-supervised learning is incorrect because the scenario does not involve a mix of labeled and unlabeled data. Regression learning is also incorrect because regression predicts continuous numerical values, while this example focuses on action selection and reward optimization. Therefore, the correct answer is C. reinforcement learning .
質問 # 42
Which of the following is NOT a common supervised learning model/algorithm?
正解：D
解説：
The correct answer is E. None of the above because K-nearest neighbors, random forest, and decision trees are all common supervised learning models or algorithms. Supervised learning uses labeled data to train a model so it can predict an output label or target value for new data.
K-nearest neighbors is a supervised learning algorithm commonly used for classification and regression. It predicts outcomes by comparing a new data point with the most similar labeled examples in the training data.
Random forest is also a supervised learning algorithm. It builds multiple decision trees and combines their results to improve prediction accuracy and reduce overfitting. Decision trees are supervised models that split data based on feature values to make classification or regression predictions.
Since options A, B, and C are all valid supervised learning algorithms, none of them is the correct example of a model that is NOT commonly supervised. Therefore, the correct answer is E. None of the above .
質問 # 43
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