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NEW QUESTION # 21
A retail company has a large dataset of customer purchases but no predefined labels. The AI system groups customers into segments based on similar buying behavior. This is an example of ______.
Answer: E
Explanation:
Unsupervised learning is the correct answer because the dataset does not contain predefined labels or known target outcomes. The AI system is identifying natural patterns in the data and grouping customers with similar purchasing behavior. This type of task is commonly called clustering, which is one of the most common applications of unsupervised learning. Supervised learning is incorrect because there are no labeled examples telling the model which customer belongs to which segment. Reinforcement learning is incorrect because the system is not learning through rewards or penalties. Transfer learning involves reusing knowledge from one trained model for another related task, which is not described here. Semi-supervised learning would involve both labeled and unlabeled data, but this scenario only mentions unlabeled data. Therefore, the correct answer is B. unsupervised learning .
NEW QUESTION # 22
Unsupervised learning is a type of machine learning where the algorithm learns from a ______.
Answer: B
Explanation:
The correct answer is B. Unlabeled dataset . Unsupervised learning is a machine learning approach where the algorithm works with data that does not contain predefined labels, target outputs, or correct answers. Instead of being told what each data point represents, the model analyzes the structure of the data and identifies hidden patterns, groupings, similarities, or relationships on its own.
This type of learning is commonly used for clustering, association rule mining, anomaly detection, dimensionality reduction, and customer segmentation. For example, an unsupervised learning model may group customers based on buying behavior without being given category labels in advance.
A labeled dataset is used in supervised learning, where the model learns from input-output pairs. An
"explained dataset" is not a standard machine learning category. Since unsupervised learning specifically depends on unlabeled data, the correct answer is B. Unlabeled dataset .
NEW QUESTION # 23
Which of the following is NOT CORRECT for the Elbow method?
Answer: C
Explanation:
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 .
NEW QUESTION # 24
What is a prompt?
Answer: D
Explanation:
The correct answer is D. a and b only because a prompt is the input provided by a user to a generative AI model. In natural language systems such as ChatGPT and other language models, the prompt is usually written as text in natural language. It may be a question, instruction, command, description, context, example, or task requirement that guides the model toward producing a response.
Statement A is correct because prompts are the user-provided input that generative models use to produce outputs. Statement B is also correct because, for ChatGPT and similar models, prompts commonly appear as natural language text. Statement C is not fully correct because prompts are an important way to guide model output, but they are not the only possible control mechanism. Outputs can also be influenced by system instructions, model settings, retrieval context, fine-tuning, guardrails, and application design. Therefore, the best answer is D. a and b only .
NEW QUESTION # 25
Select the most CORRECT statement.
Answer: E
Explanation:
The correct answer is D. a and c only because dimensionality reduction is the process of reducing the number of variables or features considered in a dataset while trying to preserve the most important information. This is especially useful when working with large datasets that contain many columns, attributes, or variables.
Reducing dimensionality can improve model performance, reduce computational cost, remove noise, and make data easier to visualize and analyze.
Statement A is correct because dimensionality reduction reduces the number of variables considered in the analysis. Statement C is also correct because Principal Component Analysis, or PCA, is one of the most common techniques used to reduce the dimensionality of large datasets. PCA transforms the original variables into a smaller set of principal components that capture most of the important variance in the data.
Statement B is not correct because dimensionality reduction is not about reducing "targetted variables." It focuses mainly on reducing input features or random variables. Therefore, the best answer is D. a and c only .
NEW QUESTION # 26
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