Microsoft Fabric Analytics Engineer Associate DP-600
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As a Microsoft Fabric Analytics Engineer Associate, you are tasked with implementing a function in a lakehouse to calculate the moving average of a time series dataset containing daily stock prices for a company. The solution must be flexible to accommodate various user requirements, including the ability to specify the type of moving average (simple or exponential) and its parameters (window size or decay factor). Additionally, the solution should be scalable to handle large datasets efficiently and comply with data governance policies. Which of the following approaches BEST meets these requirements? (Choose one option)
A
Implement a fixed function that calculates only the simple moving average with a predefined window size, ensuring simplicity and quick deployment.
B
Develop a function that exclusively calculates the exponential moving average using a default decay factor, optimizing for performance with large datasets.
C
Create a comprehensive function that computes both simple and exponential moving averages with fixed parameters, offering users a choice between two predefined methods.
D
Design a customizable function that allows users to specify the type of moving average (simple or exponential) and its parameters (window size or decay factor), providing flexibility and adaptability to various scenarios.