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NEW QUESTION # 57
Which of the following is NOT a common supervised learning model/algorithm?
Answer: A
Explanation:
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 .
NEW QUESTION # 58
Unsupervised learning is a type of machine learning where the algorithm learns from a ______.
Answer: D
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 # 59
Which of the following is a CORRECT NLP task?
Answer: E
Explanation:
The correct answer is E. a, b and c only because tokenization, Part-of-Speech tagging, and Question Answering are all valid natural language processing tasks. NLP focuses on enabling machines to process, analyze, understand, and generate human language for business and technical applications.
Tokenization is a basic NLP task where text is divided into smaller units such as words, subwords, or tokens.
This step helps models process language in a structured way. Part-of-Speech tagging is also an NLP task because it identifies the grammatical role of words, such as nouns, verbs, adjectives, and adverbs. This helps systems understand sentence structure and meaning. Question Answering is another important NLP task where a system interprets a user's question and generates or retrieves the most relevant answer from data, documents, or knowledge sources.
Since all three options represent correct NLP tasks, the best answer is E. a, b and c only .
NEW QUESTION # 60
Which of the following is a CORRECT statement for Few-shot learning?
Answer: A
Explanation:
The correct answer is D. a and b only because few-shot learning is a machine learning technique that allows a model to learn or adapt to a new task using only a small number of labeled examples. It is especially useful when collecting large labeled datasets is expensive, slow, or difficult. Instead of requiring thousands or millions of labeled records, few-shot learning depends on prior knowledge learned by the model and applies that knowledge to new examples with limited supervision.
Statement A is correct because few-shot learning is recognized as a machine learning approach. Statement B is also correct because the core idea of few-shot learning is learning from very limited labeled data. Statement C is not correct because learning from unlabeled data is more closely associated with unsupervised learning or semi-supervised learning, not the standard definition of few-shot learning. Therefore, the correct answer is D.
a and b only .
NEW QUESTION # 61
XAI stands for ______.
Answer: E
Explanation:
XAI stands for Explainable Artificial Intelligence. It refers to AI systems, models, and methods that help humans understand how an AI model reaches a decision, prediction, or recommendation. In business and responsible AI contexts, explainability is important because leaders, users, regulators, and stakeholders need to know why an AI system produced a specific result, especially in high-impact areas such as finance, healthcare, hiring, insurance, and public services.
Explainable AI supports transparency, accountability, trust, auditability, and risk management. It helps identify whether a model is relying on appropriate factors or producing biased, unfair, or unreliable outcomes.
"Extensible Artificial Intelligence" and "Exceptional Artificial Intelligence" are not standard meanings of XAI in artificial intelligence documentation. Since the accepted and correct expansion of XAI is Explainable Artificial Intelligence, the correct answer is B .
NEW QUESTION # 62
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The second version is the web-based format of the Certified Artificial Intelligence Consultant (CAIC) practice test. Browsers such as Internet Explorer, Microsoft Edge, Firefox, Safari, and Chrome support the web-based practice exam. You don't have to install excessive plugins or software to attempt this Certified Artificial Intelligence Consultant (CAIC) practice test.
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