The one thing they asked me that really stood out was how I approach data cleaning and ensuring data integrity before analysis. They gave me a scenario where the dataset had several missing values and inconsistencies across different columns. They wanted to know the steps I would take to clean and preprocess the data before diving into analysis. I explained that I would start by identifying and handling missing values using methods like imputation or removing rows, depending on the context. I'd also check for outliers and duplicates, ensuring the dataset is accurate and consistent. I emphasized that data quality is crucial for making reliable business decisions, so I prioritize automated data validation checks to maintain integrity across datasets."