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Answer: Use clustering algorithms to segment neighborhoods based on their energy consumption profiles and identify potential areas for energy efficiency improvements.
Option C is the most comprehensive approach for exploratory analytics in this context. It allows for the segmentation of data into meaningful clusters based on energy consumption profiles, which can directly inform urban planning and energy management strategies by identifying areas with high energy usage for targeted interventions. While options A, B, and D provide valuable insights into specific aspects of the data, they do not offer the broad exploratory analysis needed to address the question's requirements effectively. Clustering algorithms can also be scaled and applied cost-effectively across large datasets, making them suitable for the given scenario.
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As a Microsoft Fabric Analytics Engineer Associate, you are tasked with analyzing a dataset of energy consumption patterns in a smart city to support urban planning and energy management. The dataset includes variables such as time of consumption, location, and weather conditions. You need to identify trends, seasonal patterns, and anomalies to recommend actionable insights. Considering the need for scalability, cost-effectiveness, and compliance with data privacy regulations, which of the following approaches would be the BEST to start your exploratory analytics? Choose one option.
A
Calculate the correlation between energy consumption and weather conditions, and visualize the results using a scatter plot to identify direct relationships.
B
Perform a time series analysis to identify seasonal patterns in energy consumption and visualize the results using a line chart to understand temporal trends.
C
Use clustering algorithms to segment neighborhoods based on their energy consumption profiles and identify potential areas for energy efficiency improvements.
D
Apply anomaly detection techniques to identify unusual energy consumption patterns that may indicate equipment malfunctions or fraud, using statistical methods.