A business intelligence developer is trying to make use of Amazon Quicksight to create different types of visualizations.
One of the data sets consists of a date column which is not being recognized in QuickSight.
Which of the following can be done to ensure the data comes up properly in Quicksight with the least amount of effort?
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A. B. C. D.Answer - B.
All of the other options are invalid because they would all require additional effort.
The AWS Documentation mentions the following.
Amazon QuickSight natively supports a limited number of date formats.
However, you can't always control the format of the data provided to you.
When your data contains a date in an unsupported format, you can tell Amazon QuickSight how to interpret it.
You can do this by editing the data set, and changing the format of the column from text or numeric to date.
A screen appears after you make this change, so you can enter the format.
For example, if you are using a relational data source, you can specify MM-dd-yyyy for a text field containing '09-19-2017', so it is interpreted as 2017-09-19T00:00:00.000Z.
If you are using a nonrelational data source, you can do the same thing starting with a numeric field or a text field.
All of the other options are invalid because they would all require additional effort.
For more information on using unsupported dates, please visit the url.
https://docs.aws.amazon.com/quicksight/latest/user/using-unsupported-dates.htmlIn this scenario, the business intelligence developer is trying to make use of Amazon QuickSight to create different types of visualizations, but one of the data sets consists of a date column which is not being recognized in QuickSight.
To ensure the data comes up properly in QuickSight with the least amount of effort, we have the following options:
A. Change the date values in the data set to reflect the supported data types in Quicksight This option involves changing the date values in the data set to a format that is supported by QuickSight. However, this could require significant effort and may not be a feasible solution if there are a large number of records in the data set.
B. Change the format of the date column in the data set This option involves changing the format of the date column in the data set to a format that is supported by QuickSight. This could be a feasible solution if the date column has a consistent format that is not supported by QuickSight.
C. Create a new data set with the formatted date data type This option involves creating a new data set with the formatted date data type. This would require effort, but it could be a feasible solution if the original data set is small or if there are only a few data sets with the date column.
D. Use a Calculated field for the date column. This option involves creating a calculated field in QuickSight to convert the date column to a format that is supported by QuickSight. This could be a feasible solution if the date column has a consistent format that is not supported by QuickSight.
In summary, the best option to ensure the data comes up properly in QuickSight with the least amount of effort would be to choose option B or D. If the date column has a consistent format that is not supported by QuickSight, then option B would be the best choice. If the date column has a consistent format that is not supported by QuickSight, then option D would be the best choice.