Microsoft Azure Data Engineer Associate - DP-203
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In a data processing pipeline, you encounter a scenario where a significant portion of the incoming data has missing values in critical fields. These fields are essential for downstream analytics and reporting. What strategies would you employ to handle missing data effectively without compromising the integrity of the data?
In a data processing pipeline, you encounter a scenario where a significant portion of the incoming data has missing values in critical fields. These fields are essential for downstream analytics and reporting. What strategies would you employ to handle missing data effectively without compromising the integrity of the data?
Simulated
Explanation:
Handling missing data by interpolation techniques, such as linear interpolation or using statistical methods to estimate missing values, helps maintain the integrity of the data. This approach ensures that the data remains usable for analysis and reporting without the need to discard valuable information.