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As a Microsoft Fabric Analytics Engineer Associate, you are tasked with optimizing the fleet management process for a transportation company. The company has provided you with a comprehensive dataset that includes vehicle location, speed, and maintenance data. Your goal is to analyze this dataset to identify optimization opportunities. Considering the need for cost-effectiveness, compliance with data privacy regulations, and scalability for future data growth, which of the following techniques would you recommend? Choose the best option that encompasses all necessary aspects for a holistic analysis.
A
Implementing only geospatial analysis to visualize vehicle locations and routes, ignoring temporal patterns and predictive insights.
B
Focusing solely on time series analysis to identify patterns in vehicle speed and maintenance data, without considering spatial data or predictive analytics.
C
Applying predictive maintenance algorithms exclusively to forecast vehicle breakdowns, disregarding the importance of spatial and temporal data analysis.
D
Utilizing a combination of geospatial analysis for route optimization, time series analysis for identifying trends over time, and predictive maintenance algorithms for forecasting potential breakdowns to ensure a comprehensive approach.