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Answer: Perform a root cause analysis to identify factors contributing to delivery delays, such as traffic congestion or weather conditions, and visualize the results using a Pareto chart to prioritize issues.
Option B is the most appropriate answer for implementing diagnostic analytics in this scenario. Performing a root cause analysis to identify factors contributing to delivery delays can provide insights into the underlying issues affecting delivery efficiency. This information can inform targeted operational improvements, such as route optimization, resource allocation, and contingency planning. While options A, C, and D are also relevant techniques, they focus more on specific aspects of the data or predictive analytics rather than the diagnostic insights required in this scenario.
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As a Microsoft Fabric Analytics Engineer Associate, you are tasked with implementing diagnostic analytics for a logistics company to analyze delivery times. The company operates in multiple regions with varying traffic conditions, weather patterns, and delivery volumes. Your goal is to identify the root causes of delays to inform operational improvements. Which of the following approaches would BEST help you achieve this goal, considering the need for actionable insights and the complexity of the data? (Choose one option)
A
Calculate the average delivery time for each route and visualize the results using a box plot to identify outliers.
B
Perform a root cause analysis to identify factors contributing to delivery delays, such as traffic congestion or weather conditions, and visualize the results using a Pareto chart to prioritize issues.
C
Develop a predictive model to forecast future delivery times based on historical data and visualize the results using a time series plot to anticipate delays.
D
Use clustering algorithms to segment delivery routes based on their average delivery times and identify potential areas for optimization without analyzing specific delay causes.