4.1 Explain AI’s Role in Exploratory Data Analysis (EDA) AI-TECHNICAL-PRACTITIONER Practice Questions (Page 2)

Questions 6–10

1. 6application · medium

   A retail company wants to segment its customers into distinct groups based on purchasing behavior to tailor marketing campaigns. The dataset has many features, and the team is unsure which features matter most. Which AI technique is most suitable for this EDA task?

   • ASimple linear regression to predict total spending for each customer.
   • BData profiling to summarize each column, but without any grouping or pattern discovery.
   • CClustering algorithms to identify natural groups in the data, combined with automated visualization of the clusters.
   • DManual sorting of customers by age and income to create segments.
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2. 7application · medium

   A data analyst is preparing a dataset for a predictive model and wants to identify which features are most important for the target variable. The analyst also wants to visualize the relationships between the top features and the target. Which AI-based approach is most efficient?

   • AUse feature importance ranking to select top features, then generate automated visualizations for those features.
   • BTrain a model with all features and then use the model's coefficients as the only output.
   • CCreate scatter plots for every feature against the target, then manually decide which are important.
   • DUse a clustering algorithm to group features and then pick one from each group.
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4. 8expert · hard

   A data analyst is using an AI-powered visualization tool that automatically generates charts. The tool produces a complex 3D scatter plot that is difficult to interpret. What is the best course of action?

   • AAsk the AI tool to automatically interpret the 3D plot and provide a written summary.
   • BRequest simpler visualizations, such as 2D scatter plots or heatmaps, that are easier to interpret.
   • CAbandon visualization altogether and rely solely on numerical summaries.
   • DUse the 3D plot as is, since the AI generated it and it must be relevant.
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5. 9foundation · easy

   What is a common limitation of using AI in EDA?

   • AAI results can be biased if the training data is biased
   • BAI can only work with structured data
   • CAI always provides fully interpretable explanations
   • DAI eliminates the need for human oversight
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6. 10foundation · easy

   What is a key characteristic of iterative hypothesis testing with AI?

   • AIt produces final conclusions without further analysis
   • BIt requires testing all hypotheses in a single pass
   • CIt only works with small datasets
   • DIt involves repeatedly refining hypotheses based on new insights from the data
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