Your team is developing a feature that is used to enhance the production usage rate. A DynamoDB table has been implemented to trace relevant activities such as how the feature helps boosting the usage frequency. The table has about 2k new items every day and keeps increasing. Your PM wants to have a look at the daily data in this DynamoDB table. How do you present the data to PM in a proper way?

Answer options:

A.Create a new Data Pipeline to transfer the data from DynamoDB table to a S3. Activate the pipeline every day and then open the files in S3 using an editor.
B.In the DynamoDB console, select all items, use the “Export to .csv” feature to download the whole data every day. Then use data analysis tool to manage the data.
C.Create a new Data Pipeline to transfer the data from DynamoDB table to a S3. Add a schedule including a start time (8:00 AM) and end time (8:00PM) for this pipeline to run every day and collect files in S3 and get them presented to PM.
D.Create a new Data Pipeline to transfer the data from DynamoDB table to a S3 bucket. Add a schedule for this pipeline to activate it every day at a suitable time and then open the collected files in S3.

Option D is Correct:
The AWS Data Pipeline lets you automate the movement and processing of any amount of data using data-driven workflows and built-in dependency checking.
You can now choose between the following options for each pipeline that you build:
Run once
Run a defined number of times
Run on activation
Run indefinitely
Run repeatedly within a date range
When a Data Pipeline is being created, you could add a schedule such as starting every day with a start time:
To end the pipeline, you could use occurrences or a specific date. For example, the schedule will stop after 10 times or after 2 weeks.
Option A is incorrect: because in this case as the daily data is needed, the Data Pipeline should include a schedule to run otherwise it has to be run manually every day, which is not necessary.
Option B is incorrect: because you could only choose 100 items in a batch and export to a csv file. Considering that the table has 2k data and keeps increasing. This approach is not reasonable.
Option C is incorrect: because the end time is not used properly. For the schedule in Data Pipeline, the end date/time is a date/time that the whole schedule stops such as one month later. In this case, the end time of “8:00PM” is invalid and not required. Instead, you could add an ending date in which the whole schedule stops as required. Please also refer to https://docs.aws.amazon.com/datapipeline/latest/DeveloperGuide/dp-object-schedule.html
on how to use the end time.