Google Data Analytics Professional Certification Practice Test

In which scenario would you use verification in data analysis?

1. To create a summary report for stakeholders

2. To confirm the accuracy and reliability of data post-cleaning

3. To format data for presentation

4. To collect data from multiple sources

The correct answer is: To confirm the accuracy and reliability of data post-cleaning

Using verification in data analysis is essential when confirming the accuracy and reliability of data after it has undergone cleaning. This process involves validating that the data has been correctly processed and that any errors or anomalies have been addressed. Verification ensures that the data is trustworthy and fit for analysis, which is critical in making informed decisions based on that data. In the context of data analysis, cleaning involves removing inaccuracies, duplicates, and irrelevant information from the dataset. Once this cleaning process is complete, verification comes into play to check that the data reflects the intended quality and can lead to reliable insights. Without this step, any conclusions drawn from potentially flawed data could misinform stakeholders and lead to misguided strategies. The other options pertain to different aspects of the data analysis process. Creating a summary report for stakeholders relates to presenting insights effectively, while formatting data for presentation focuses on the aesthetic and structural presentation of the data rather than its underlying accuracy. Collecting data from multiple sources involves aggregation and integration of information, but does not necessarily confirm its reliability. Verification specifically targets the integrity of the cleaned data, emphasizing its importance in the analytical workflow.