A data analyst for a retail company merges website clickstream data with point-of-sale (POS) transaction logs to analyze marketing campaign effectiveness. The analyst needs to ensure that the records from both sources correspond correctly after the merge. Which method should the analyst use to verify this alignment?
Conducting a data audit is the correct approach because it involves a systematic examination of data to verify accuracy and integrity, which includes comparing records from different sources to ensure they align correctly. Cross-validation is a technique used in machine learning to evaluate how a model will perform on unseen data, not to align two different datasets. Data profiling summarizes the structure and content of a single dataset but does not verify alignment between two sources. A sample check involves reviewing only a small subset of records and could easily miss widespread alignment issues.
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What is cross-validation in the context of data alignment?
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How does cross-validation differ from data profiling?
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Why is randomly sampling entries not effective for verifying data alignment?