<p dir="ltr" style="line-height: 1.38; margin-top: 12pt; margin-bottom: 12pt;">[size= 11pt; font-family: Arial,sans-serif; color: #000000; background-color: transparent; font-weight: 400; font-style: normal; font-variant: normal; text-decoration: none; vertical-align: baseline; white-space: pre-wrap]DAX (Data Analysis Expressions) is a powerful language in Power BI but presents challenges due to its steep learning curve, performance issues, and complex calculations. Writing optimized DAX queries requires a strong understanding of context transitions, filter propagation, and calculation dependencies. Power BI developers often struggle with iterators, circular dependencies, and inefficient measures. To overcome these issues, they must use variables, optimize calculated columns vs. measures, and leverage best practices like using SUMX over SUM. Tools like DAX Studio, SQL Profiler, and Power BI Performance Analyzer can help in debugging and performance tuning DAX expressions.[/size]