4.1 Explain AI’s Role in Exploratory Data Analysis (EDA) AI-TECHNICAL-PRACTITIONER Practice Questions (Page 5)

Questions 21–25

1. 21foundation · easy

   What is the purpose of AI-based imputation in EDA?

   • ATo fill in missing values using patterns learned from the data
   • BTo replace missing values with random numbers
   • CTo ignore missing values during analysis
   • DTo delete all rows with missing data
   Reveal Solution Discussion Select an answer first
2. 22foundation · easy

   How can AI help filter noise from a dataset during EDA?

   • ABy removing all data points that are not perfectly consistent
   • BBy identifying and smoothing out random variations that obscure true patterns
   • CBy adding more random noise to balance the dataset
   • DBy converting all numeric data to categorical data
   Reveal Solution Discussion Select an answer first
4. 23foundation · easy

   Which of the following best describes a key benefit of using AI in exploratory data analysis?

   • AIt eliminates the need for human analysts entirely
   • BIt guarantees that all data is perfectly clean and error-free
   • CIt accelerates the discovery of patterns and insights in large datasets
   • DIt replaces the need for data visualization entirely
   Reveal Solution Discussion Select an answer first
5. 24foundation · easy

   Why is feature selection important in EDA?

   • AIt reduces dimensionality and can improve model performance
   • BIt guarantees that the model will be 100% accurate
   • CIt increases the number of features to capture more information
   • DIt eliminates the need for data cleaning
   Reveal Solution Discussion Select an answer first
6. 25expert · hard

   A healthcare organization is using an AI tool to profile patient data for EDA. The tool flags a high rate of missing values in a demographic variable and suggests imputing them. The data team is concerned about introducing bias. What is the most appropriate action?

   • AIgnore the missing data and proceed with the analysis without any handling.
   • BInvestigate the pattern of missingness and consult domain experts before deciding on an imputation strategy.
   • CDelete all records with missing demographic data to avoid bias.
   • DImpute the missing values using the AI tool's default method and proceed.
   Reveal Solution Discussion Select an answer first