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When monitoring models in production, what aspect is crucial for maintaining long-term reliability?
When monitoring models in production, what aspect is crucial for maintaining long-term reliability?A . Regularly updating the user interfaceB . Ensuring the model is scalable to handle increased loadsC . Reducing the number of inputs to the modelD . Focusing solely on increasing model speedView AnswerAnswer: B
Which of the following are considered direct effects of an AI solution? (Choose Two)
Which of the following are considered direct effects of an AI solution? (Choose Two)A . Enhancements in process efficiency for which the AI was designedB . Increased job satisfaction among employees not using the AI directlyC . Reduction in operational costs due to automationD . New market opportunities stemming from...
For a model that needs to explain itself, which methods could be appropriately used? (Choose Three)
For a model that needs to explain itself, which methods could be appropriately used? (Choose Three)A . LIME (Local Interpretable Model-agnostic Explanations)B . SHAP (SHapley Additive exPlanations)C . Embedding model parameters directly into the user interfaceD . Feature importance scoresE . Randomizing input features to test output variationView AnswerAnswer: ABD
How do you assess the feasibility of an AI solution?
How do you assess the feasibility of an AI solution?A . By evaluating the available technology and resourcesB . By creating detailed financial models onlyC . By ensuring the project is the top priority of the organizationD . By hiring external consultants to validate the solutionView AnswerAnswer: A
How does feature scaling benefit the process of exploratory data analysis?
How does feature scaling benefit the process of exploratory data analysis?A . It changes the underlying data distributionB . It makes different variables comparableC . It simplifies the database management systemD . It eliminates the need for data cleaningView AnswerAnswer: B
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
