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How can ensemble modeling improve machine learning performance?
How can ensemble modeling improve machine learning performance?A . By simplifying the models to reduce computation timeB . By combining multiple models to reduce variance and biasC . By using a single, highly accurate modelD . By focusing exclusively on increasing model accuracyView AnswerAnswer: B
Which of the following is a common use case for recommendation engines?
Which of the following is a common use case for recommendation engines?A . Predicting property pricesB . Detecting fraudulent credit card transactionsC . Suggesting products to customers based on past purchasesD . Categorizing news articles into topicsView AnswerAnswer: C
In K-Nearest Neighbors (KNN), what does K represent?
In K-Nearest Neighbors (KNN), what does K represent?A . The number of clusters to formB . The number of training samples to useC . The number of features to considerD . The number of nearest neighbors to considerView AnswerAnswer: D
Which of the following are essential tasks when preparing data for exploratory analysis? (Choose Three)
Which of the following are essential tasks when preparing data for exploratory analysis? (Choose Three)A . Labeling data accuratelyB . Ensuring data is representative of the entire populationC . Assigning random values to missing data pointsD . Anonymizing sensitive informationE . Organizing data chronologicallyView AnswerAnswer: ABD
Which practice is least effective in configuring environments for training machine learning models?
Which practice is least effective in configuring environments for training machine learning models?A . Using virtual environments to manage dependenciesB . Using the latest but unstable software versionsC . Regularly updating libraries to their stable versionsD . Allocating resources based on model requirementsView AnswerAnswer: B
Why is it important to create hypotheses about the behavior of the AI system?
Why is it important to create hypotheses about the behavior of the AI system?A . It simplifies the coding process for developersB . It helps predict and mitigate potential risks associated with the systemC . It is primarily for marketing purposesD . It fulfills a legal requirement for AI developmentView...
For implementing dimensional reduction, which method would be most effective when dealing with highly nonlinear data?
For implementing dimensional reduction, which method would be most effective when dealing with highly nonlinear data?A . Linear Discriminant Analysis (LDA)B . PCAC . t-Distributed Stochastic Neighbor Embedding (t-SNE)D . Factor AnalysisView AnswerAnswer: C
Which is a primary goal of AI design thinking in relation to business problems?
Which is a primary goal of AI design thinking in relation to business problems?A . Maximizing the speed of developmentB . Understanding user needs and pain pointsC . Choosing the most advanced AI technologiesD . Ensuring the project is completed under budgetView AnswerAnswer: B
Why is logistic regression considered a linear classifier?
Why is logistic regression considered a linear classifier?A . Because it is only capable of linear regression tasksB . Because it uses a linear decision boundary to separate classesC . Because it applies a nonlinear transformation to the input featuresD . Because it computes the decision boundary using a non-linear...
Given an SQL table 'Books' with fields Title, Author, and Genre, which query would return a list of unique Genre values?
Given an SQL table 'Books' with fields Title, Author, and Genre, which query would return a list of unique Genre values?A . SELECT Genre FROM Books;B . SELECT SET Genre FROM Books;C . SELECT UNIQUE Genre FROM Books;D . SELECT DISTINCT Genre FROM Books;View AnswerAnswer: D
