DBS-C01 Workload-Specific Database Design Practice Question
A financial services company uses Amazon DynamoDB to store transaction records. The table has a partition key of 'AccountId' and a sort key of 'TransactionDate'. The company needs to run analytical queries that aggregate transactions by account and month. Currently, queries are slow due to full table scans. Which design change will improve query performance most effectively?
⚠ Common exam trap
Many exam-takers confuse caching solutions like DAX with query optimization, not realizing that DAX does not change the access pattern or eliminate the need for scans when the query lacks an appropriate index.
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
✓
Create a Global Secondary Index (GSI) with partition key AccountId and sort key Month.
Creating a Global Secondary Index (GSI) with partition key AccountId and sort key Month allows the analytical queries to efficiently retrieve aggregated data by account and month without scanning the entire base table. The GSI reorganizes data by month, enabling DynamoDB to use the sort key for range queries and avoid full table scans, which directly addresses the performance issue.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Add DynamoDB Accelerator (DAX) to the table.
Why it's wrong here
DAX caches individual items, not aggregation queries.
- ✗
Change the table's sort key from TransactionDate to Month.
Why it's wrong here
Loses the ability to query by exact date.
- ✗
Enable DynamoDB Streams and process the stream with AWS Lambda to pre-aggregate results.
Why it's wrong here
Does not improve query performance; offloads computation.
- ✓
Create a Global Secondary Index (GSI) with partition key AccountId and sort key Month.
Why this is correct
Allows efficient aggregation queries using the GSI.
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