Google Data Analytics Professional Certification Practice Test

What does the process of verification ensure in data cleaning?

1. That the data is documented correctly

2. That data is aggregated properly

3. That the data-cleaning effort was well-executed and results are reliable

4. That all errors have been eliminated from the dataset

The correct answer is: That the data-cleaning effort was well-executed and results are reliable

In data cleaning, the process of verification is crucial for ensuring that the data-cleaning effort was executed effectively and that the resulting data is reliable for analysis. Verification typically involves checking that the cleaning processes have been performed according to the established protocols and that the data meets quality standards necessary for analysis. This includes confirming that the transformations applied—such as handling missing values, correcting errors, and standardizing formats—have been carried out properly and led to a dataset that accurately represents the underlying information without significant inconsistencies or distortions. By focusing on this aspect, stakeholders can be more confident in the insights derived from the data and its subsequent use in decision-making processes. While other processes, such as documentation and aggregation, are important in the data preparation workflow, they do not encapsulate the specific assurance of reliability that verification provides in the context of data cleaning. Furthermore, while striving to eliminate all errors is ideal, complete elimination of errors is often impractical, making verification focus more on the reliability of the cleaned data rather than a guarantee that every single error has been addressed.