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NEW QUESTION # 68
Which of the following is a common supervised learning model/algorithm?
Answer: D
Explanation:
The correct answer is D. All of the above because Naive Bayes classifier, Support Vector Machine, and linear regression are all commonly used supervised learning algorithms. Supervised learning uses labeled training data, where the model learns the relationship between input features and known output labels or target values.
Naive Bayes is a supervised classification algorithm commonly used for text classification, spam detection, sentiment analysis, and document categorization. Support Vector Machine is also a supervised learning algorithm used for classification and regression tasks by finding an optimal boundary or hyperplane between classes. Linear regression is a supervised learning model used for predicting continuous numeric values, such as sales, prices, demand, or costs, based on input variables.
Since all three listed options are valid examples of supervised learning models or algorithms, the most complete and correct answer is D. All of the above .
NEW QUESTION # 69
Which of the following statement is CORRECT for RNN?
Answer: E
Explanation:
The correct answer is E. a, b and c only because all three statements correctly describe Recurrent Neural Networks and their limitation. RNNs are neural network models designed for sequential data such as text, speech, time-series data, and ordered events. They process information step by step and use previous hidden states to influence later outputs.
Statement A is correct because a major drawback of traditional RNNs is their difficulty in remembering information over many time steps. This happens mainly because of vanishing gradient problems during training. Statement B is also correct because standard RNNs generally struggle with long-term dependencies, meaning they may fail to retain important information from earlier parts of a sequence. Statement C is correct because Long Short-Term Memory networks are a specialized extension of RNNs designed to handle long- term memory more effectively using gates that control what information is stored, forgotten, and passed forward.
Therefore, the best answer is E. a, b and c only .
NEW QUESTION # 70
What is the main advantage of using deep learning over traditional machine learning?
Answer: C
NEW QUESTION # 71
Select the most INCORRECT risk-scoring methodology function statement for retrospective/concurrent.
Answer: A
Explanation:
The correct answer is D. a and b only because statements A and B are the most incorrect for retrospective
/concurrent risk-scoring methodology. Retrospective/concurrent risk assessment is mainly used to evaluate model risk based on past or present evidence, current model behavior, observed incidents, model performance changes, risk indicators, and investigation findings. It is not primarily a future-prediction method.
Statement A is incorrect because it says retrospective/concurrent methods "predict" model risk after analyzing historical model performance. Historical performance may be reviewed, but retrospective/concurrent risk scoring is more about assessing or investigating past and current risk conditions, not predicting future risk.
Statement B is also incorrect because using current model risk to predict overall model risk for future cycles describes prospective risk, not retrospective/concurrent risk. Statement C is correct because retrospective
/concurrent review is suitable when there are changes in model behavior, risk indicators, attacks, data loss, or investigation needs. Therefore, the most incorrect statements are A and B only .
NEW QUESTION # 72
Which of the following is NOT a common supervised learning model/algorithm?
Answer: D
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 # 73
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