### Q1: What is one easy technique you can use to identify and remove duplicate entries in your datasets?  
### Q2: What is a moderate strategy for handling missing values in data, and how does it impact your analysis?  
### Q3: What is a difficult consideration to keep in mind when selecting a data cleaning tool, and how can it affect the accuracy of your insights?  
### Q4: How can you apply a low-code approach to standardize values in your dataset effectively?  
### Q5: What is a moderate business application of data cleaning techniques that can enhance customer engagement in a B2C context?  
### Q6: Can you explain a difficult scenario where data cleaning directly influenced a business decision?  
### Q7: How can clear communication of data cleaning processes improve team collaboration?  
### Q8: What is a moderate way to articulate the importance of data quality to stakeholders?  
### Q9: How can you effectively communicate complex data cleaning techniques to a non-technical audience?