Microsoft Fabric Analytics Engineer Associate DP-600
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You are a Microsoft Fabric Analytics Engineer working on a project to analyze a large dataset of customer transaction records for a retail company. The dataset contains millions of records with various numerical and categorical variables. Your goal is to ensure a thorough and comprehensive analysis to identify trends, anomalies, and opportunities for business improvement. The company emphasizes the importance of data quality and accuracy in their analysis. Considering the need for a detailed profiling of the dataset, which of the following steps should you prioritize to address potential data quality issues before proceeding with advanced analytics? Choose the best option.
Explanation:
Option B is the most appropriate answer because a thorough data quality assessment is foundational to any data analysis project. Identifying and rectifying issues like missing values, outliers, and inconsistencies early on ensures that the dataset is accurate and reliable for any subsequent analysis, including statistical summaries, visualizations, or machine learning applications. While options A, C, and D are valuable steps in the data analysis process, they should be performed after ensuring the data's quality to avoid misleading results or conclusions based on flawed data.