DBS-C01 Workload-Specific Database Design Practice Question
A retail company uses Amazon DynamoDB to store shopping cart data. The cart items are frequently updated as users add or remove products. The application reads the entire cart each time the user views it. The cart size averages 50 KB but can reach up to 400 KB. The company wants to reduce read costs and improve performance. Which design change would be most effective?
⚠ Common exam trap
Test-takers frequently assume caching (DAX) is the universal performance fix, but the question specifically targets reducing read costs, not just latency, and DAX does not eliminate the underlying cost of reading large items from DynamoDB.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Compress the cart items before storing them in DynamoDB and decompress on read.
Compressing cart items before storing them in DynamoDB reduces the item size, which directly lowers read capacity unit (RCU) consumption since DynamoDB charges based on read item size rounded up to 4 KB increments. For a 400 KB item, compression can shrink it significantly, reducing the number of 4 KB blocks read and thus cutting costs. Decompression on read adds minimal CPU overhead but yields substantial performance gains by reducing network transfer time and read latency.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Switch to larger DynamoDB instance types to handle larger items.
Why it's wrong here
DynamoDB is serverless; there are no instance types.
- ✗
Use DynamoDB Accelerator (DAX) to cache the cart data.
Why it's wrong here
DAX improves read performance but does not reduce read costs; it adds additional cost.
- ✓
Compress the cart items before storing them in DynamoDB and decompress on read.
Why this is correct
Compression reduces the item size, lowering RCU consumption and cost.
- ✗
Normalize the cart data into separate tables for cart headers and line items.
Why it's wrong here
Normalization increases the number of read operations, increasing costs.
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