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Question 1 of 4
AutoML always produces more accurate models than manual approaches
AutoML significantly reduces development time by automating repetitive tasks
AutoML requires no data preparation or cleaning whatsoever
AutoML eliminates the need for data scientists completely
Question 2 of 4
It removes the need for hyperparameters entirely
It tests various combinations of hyperparameters automatically to find optimal settings
It requires users to manually specify all hyperparameters
It uses the same hyperparameters for all machine learning models
Question 3 of 4
AutoML cannot handle datasets larger than 1GB
AutoML typically provides less customization for specialized domains or unique problems
AutoML only works with numerical data, not text or images
AutoML requires more computing power than manual approaches
Question 4 of 4
In which scenario would a manual machine learning workflow likely be more appropriate than AutoML?
When working with a standard classification problem and a clean, well-structured dataset
When needing quick results for a proof-of-concept project
When dealing with a complex, niche dataset requiring specialized domain knowledge and custom feature engineering
When working with limited computing resources on a small dataset