Complete DA0-001 CompTIA Materials

In data management,duplicate datarefers to identical records that appear multiple times within a dataset. Such duplicates can lead to inaccurate analyses, inflated metrics, and erroneous business decisions. Identifying and removing duplicate records is a critical step in the data cleansing process to ensure data quality and reliability.

Option A:Duplicate data

Rationale:The dataset shows that the record with ID 376, Amount $400, and SKU ABV-DYH appears twice. This repetition indicates the presence of duplicate data, which can skew analysis results if not addressed.

Option B:Imputed data

Rationale:Imputed data refers to missing or incomplete data that has been estimated or filled in based on other available information. There is no evidence in the provided dataset to suggest that any data has been imputed.

Option C:Redundant data

Rationale:Redundant data involves unnecessary repetition of data across different fields or tables, leading to inefficiencies. While duplicate data is a form of redundancy, in this context, the specific issue is the exact repetition of entire records, making "duplicate data" the more precise term.

Option D:Corrupt data

Rationale:Corrupt data refers to data that has been altered or damaged, making it incorrect or unusable. The dataset provided does not exhibit signs of corruption, such as garbled text or invalid formats.

A derived variable is created through mathematical computations or transformations from existing data. In this case, "income per day" is derived by combining multiple existing variables​.

The ‘Status’ column in the dataset shows different terms such as “yes”, “completed”, “done”, and “Y” that likely represent the same outcome - that a task has been completed. This variation in terms leads to inconsistency within the data. Data profiling aims to ensure that data is consistent, among other quality metrics, to facilitate accurate analysis and reporting. By recoding the ‘Status’ column, the analyst seeks to address this inconsistency, ensuring that all entries indicating completion are represented uniformly. This enhances the data quality and usability for subsequent data analysis tasks.References:

The action of recoding is taken to standardize the data entries and eliminate inconsistencies, which is crucial for maintaining data integrity and ensuring reliable data analysis.