Questions

1. What are the two essential techniques discussed in the chapter for cleaning and preparing data using the Query Editor in Power BI?
   1. Fuzzy matching and fill up
   2. Data profiling and sorting
   3. Fuzzy matching and fill down
   4. Data imputation and statistical analysis
2. In the context of fuzzy matching, what is the similarity score range, and what does it indicate?
   1. Range from 1 to 10, indicating similarity strength
   2. Range from 0 to 100, indicating confidence level
   3. Range from 0 to 1, indicating no to perfect similarity
   4. Range from -1 to 1, indicating negative to positive correlation
3. When is the fill down technique in Power BI’s Query Editor particularly useful?
   1. When you want to skip data gaps
   2. When dealing with categorical data
   3. When you need to perform calculations on filled values
   4. When working with time series data and maintaining data continuity
4. What is a crucial best practice emphasized when working with fuzzy matching and fill down in Power BI?
   1. Occasionally document the steps...