Google Professional Data Engineer Question 178
Select 3Google Cloud PlatformYou are building a data pipeline to prepare data for visualization in Google Looker Studio. Your raw data contains fields with inconsistent date formats, null values in critical columns, and some outlier values in numeric fields. Which actions should you take to ensure the data is properly prepared for visualization?
- A
Standardize all date fields to a single format.
- B
Remove all outlier data to ensure clean visualizations.
- C
Fill or interpolate null values in critical columns.
- D
Aggregate the data to reduce its size before visualization.
- E
Validate and clean inconsistent data formatting in critical columns.
Show answer and explanation
Correct answers: A, C, E
Explanation
To prepare data for visualization, it is critical to ensure that the data is clean, consistent, and complete. Standardizing date formats, handling null values, and cleaning inconsistent formatting are essential steps that directly impact the quality and usability of visualizations. While outliers and aggregation might be relevant in specific scenarios, they are not universally required actions for preparing data for visualization.
- A. Correct.
Correct: Standardizing date fields to a single format ensures consistency, making it easier to group, filter, or analyze the data in visualization tools.
- B. Incorrect.
Incorrect: While outliers can sometimes distort visualizations, they should not always be removed as they can represent valuable insights. Consider flagging them instead.
- C. Correct.
Correct: Filling or interpolating null values in critical columns ensures that visualizations are complete and do not break due to missing data.
- D. Incorrect.
Incorrect: Aggregating data is not always necessary for visualization and depends on the specific use case. Additionally, aggregation might remove important details required for the analysis.
- E. Correct.
Correct: Validating and cleaning inconsistent data formatting is crucial for ensuring accurate visualizations, as inconsistent data can result in errors or misinterpretations.