- All Exams Instant Download
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
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 approach is recommended for prioritizing business opportunities when planning an MVP?
Which approach is recommended for prioritizing business opportunities when planning an MVP?A . Choosing the most straightforward implementation irrespective of impactB . Assessing the potential return on investment and strategic fitC . Prioritizing based on the preference of the project managerD . Focusing solely on technological innovationView AnswerAnswer: B
What are two reasons a data point would be treated as an outlier?
What are two reasons a data point would be treated as an outlier?A . If the value is greater than meanB . If the value is greater than medianC . If the value is greater than standard deviationD . If the value is below the upper end of the bottom...
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
In the context of machine learning, what does the term 'model drift' refer to?
In the context of machine learning, what does the term 'model drift' refer to?A . The migration of the model from one server to anotherB . The change in model parameters due to new updatesC . The change in model performance due to changes in underlying data patternsD . The...
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
In the context of classification, what does the term 'overfitting' refer to?
In the context of classification, what does the term 'overfitting' refer to?A . The model performs equally well on the training and test datasetsB . The model performs poorly on both training and test datasetsC . The model performs too well on the training dataset but poorly on unseen dataD...
What is the primary purpose of monitoring a model in production?
What is the primary purpose of monitoring a model in production?A . To enhance the visual appeal of the model's outputB . To ensure the model's performance remains stable over timeC . To reduce the model's complexity for easier understandingD . To increase the model's training speedView AnswerAnswer: B
What considerations should be made when evaluating the ethical implications of a business problem? (Choose Three)
What considerations should be made when evaluating the ethical implications of a business problem? (Choose Three)A . Potential for AI to replace human jobsB . Environmental impact of AI solutionsC . Impact on company profit marginsD . Consequences for user privacy and autonomyE . Speed of implementationView AnswerAnswer: ABD
