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Which practice contributes to optimizing data storage in a warehouse or lakehouse by reducing unnecessary data elements without data loss?
What is the primary role of hierarchies in semantic models when considering data analysis and visualization?
Note: This question is part of a series of questions that present the same scenario. Each question in the series contains a unique solution that might meet the stated goals. Some question sets might have more than one correct solution, while others might not have a correct solution.
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You have a Fabric tenant that contains a new semantic model in OneLake.
You use a Fabric notebook to read the data into a Spark DataFrame.
You need to evaluate the data to calculate the min, max, mean, and standard deviation values for all the string and numeric columns.
Solution: You use the following PySpark expression:
df.explain()
Does this meet the goal?
What method involves transforming data from a wide format to a long format, or vice versa, in a warehouse or lakehouse environment?
Which action contributes to optimizing enterprise-scale semantic models for efficient query performance? (Select all that apply)
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