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In a lakehouse architecture, which component provides a unified interface for managing and querying data stored in various formats and locations?
What considerations are essential in planning a data analytics environment for effective scalability? (Select all that apply)
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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?
Which strategy is employed to optimize enterprise-scale semantic models for efficient data retrieval and storage?
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