Google Professional Data Engineer Question 177
Select 3Google Cloud PlatformYou are designing a data pipeline on Google Cloud to prepare data for visualization in a business intelligence tool. The data originates from multiple sources with varying schemas and includes some incomplete and inconsistent records. What steps should you take to ensure the data is ready for effective visualization?
- A
Standardize the schemas across all data sources.
- B
Remove all incomplete records from the dataset.
- C
Clean and normalize the data to ensure consistency.
- D
Aggregate the data to reduce the dataset size.
- E
Validate the data quality before loading it into the visualization tool.
Show answer and explanation
Correct answers: A, C, E
Explanation
Preparing data for visualization involves steps such as standardizing schemas, cleaning and normalizing data, and validating data quality. These steps ensure that the data is consistent, reliable, and ready to be visualized effectively. Removing incomplete records or aggregating data may not always be the best approach, as they depend on the specific context and requirements of the analysis.
- A. Correct.
Standardizing schemas ensures that data from different sources can be combined and visualized cohesively, which is critical for accurate and meaningful visualizations.
- B. Incorrect.
Removing all incomplete records is not always advisable as it can lead to loss of valuable information. Instead, incomplete data should be handled appropriately (e.g., imputation or flagging).
- C. Correct.
Cleaning and normalizing the data ensures that inconsistencies, such as differing date formats or units, are resolved, which is crucial for accurate visualizations.
- D. Incorrect.
Aggregating data to reduce its size is not always required and depends on the visualization needs. Over-aggregation can lead to a loss of detail that might be necessary for insights.
- E. Correct.
Validating data quality ensures that the data loaded into the visualization tool is reliable and accurate, which is essential for producing meaningful visualizations.