Amazon BDS-C00 Exam - Topic 5 Question 120 Discussion

Actual exam question for Amazon's BDS-C00 exam

Question #: 120
Topic #: 5

An online retailer is using Amazon DynamoDB to store data relate to customer transactions. The items in the table contain several string attributes describing the transaction as well as a JSON attribute containing the shopping cart and other details corresponding to the transactions. Average item size is ~250KB, most of which is associated with the JSON attribute. The average generates ~3GB of data per month.

Customers access the table to display their transaction history and review transaction details as needed. Ninety percent of queries against the table are executed when building the transaction history view, with the other 10% retrieving transaction details. The table is partitioned on CustomerID and sorted on transaction data.

The client has very high read capacity provisioned for the table and experiences very even utilization, but complains about the cost of Amazon DynamoDB compared to other NoSQL solutions.

Which strategy will reduce the cost associated with the client's read queries while not degrading quality?

AModify all database calls to use eventually consistent reads and advise customers that transaction history may be one second out-of-date.

BChange the primary table to partition on TransactionID, create a GSI partitioned on customer and sorted on date, project small attributes into GSI and then query GSI for summary data and the primary table for JSON details.

CVertically partition the table, store base attributes on the primary table and create a foreign key reference to a secondary table containing the JSON data. Query the primary table for summary data and the secondary table for JSON details.

DCreate an LSI sorted on date project the JSON attribute into the index and then query the primary table for summary data and the LSI for JSON details

Show Suggested Answer

Suggested Answer: C

by Franklyn at Aug 01, 2025, 12:45 PM

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Rasheeda

2 months ago

C could simplify things and save money too!

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Yuriko

3 months ago

Wait, are we really considering changing the primary table?

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Beth

3 months ago

B seems complicated, but it could work.

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Cherelle

3 months ago

I disagree, eventually consistent reads might confuse customers.

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Carissa

4 months ago

A sounds like a good way to cut costs!

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Sage

4 months ago

I vaguely recall something about LSIs and how they can help with query efficiency. If we project the JSON attribute into the index, it might save costs, but I wonder if it would still handle the read capacity well.

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Herschel

4 months ago

Vertically partitioning the table sounds like a good idea, but I’m uncertain about how it would affect query performance. We did a case study on this, and it seemed beneficial in some scenarios.

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Mila

4 months ago

I think changing the primary table's partitioning could help, but it sounds complicated. We practiced a similar question where we had to optimize queries using GSIs, and it was tricky to balance performance and cost.

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Lindsey

4 months ago

I remember we discussed using eventually consistent reads in class. It seems like a simple way to cut costs, but I'm not sure if customers would be okay with the slight delay in data accuracy.

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Ciara

5 months ago

I'm leaning towards the LSI option, since it sounds like it could provide a good balance between cost savings and maintaining data quality. Querying the primary table for the summary data and the LSI for the JSON details seems like a solid approach. I'll need to make sure I understand how to set up and maintain the LSI effectively.

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Naomi

5 months ago

The vertical partitioning option seems like it could work well too, separating the base attributes and the JSON data. That way we can query the primary table for the summary data and only retrieve the full JSON details when needed. I'll need to think through the implementation details.

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Emmett

5 months ago

Okay, the second option with the GSI partitioned on customer and sorted on date sounds promising. Projecting the small attributes into the GSI and then querying that for the summary data could be a good way to optimize the reads. I'll need to make sure I understand how that would work in practice.

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Troy

5 months ago

Hmm, the eventually consistent reads option seems tempting to reduce costs, but I'm not sure if the customers will be okay with potentially out-of-date transaction history. I'll have to weigh the trade-offs carefully.

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Royce

5 months ago

This is a tricky one. I'm not sure if I fully understand the implications of the different options. I'll need to carefully read through each one and think it through.

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Rickie

6 months ago

That's true, but it might impact the quality of the data for some customers.

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Inocencia

6 months ago

Vertically partitioning the table and using a secondary table for the JSON data is an interesting approach, but it might add complexity to the application.

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Cordell

2 months ago

Maybe a simpler solution like option A would work better?

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Roslyn

2 months ago

True, but it might save costs in the long run.

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Keneth

3 months ago

I agree, vertically partitioning could complicate things.

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Desmond

3 months ago

Complexity can lead to maintenance issues later.

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Annice

7 months ago

Partitioning on TransactionID and using a GSI sounds like a good way to optimize the query performance and cost. The JSON data can be efficiently retrieved from the GSI.

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Beth

7 months ago

The eventual consistency option seems like a reasonable trade-off, but I'm not sure if customers will be happy with potentially outdated transaction history.

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Laurel

6 months ago

B) Change the primary table to partition on TransactionID, create a GSI partitioned on customer and sorted on date, project small attributes into GSI and then query GSI for summary data and the primary table for JSON details.

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