SAP-C02 Design for New Solutions Practice Question
A startup is building a serverless application using AWS Lambda for business logic and Amazon DynamoDB for data storage. The application must process a high volume of writes to a single DynamoDB table. The development team is concerned about throttling due to hot partitions. Which design should the team implement to avoid throttling?
⚠ Common exam trap
Watch out — candidates often confuse caching (DAX) as a solution for write performance, not realizing DAX only accelerates reads, or they mistakenly believe that secondary indexes (GSI/LSI) can redistribute write load, when in fact they share the base table's partition key and do not solve hot partition issues.
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
✓
Use a composite primary key with a partition key that has high cardinality, such as a user ID.
Using a partition key with high cardinality, such as a user ID, ensures that write requests are evenly distributed across all partitions in the DynamoDB table. This prevents any single partition from becoming a hot partition, which would otherwise lead to throttling when the partition's throughput capacity is exceeded. DynamoDB scales by splitting partitions based on the partition key's hash, so high cardinality is essential for avoiding throttling under high write volumes.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Enable DynamoDB Accelerator (DAX) to cache write operations.
Why it's wrong here
DAX is a read cache; it does not prevent write throttling.
- ✓
Use a composite primary key with a partition key that has high cardinality, such as a user ID.
Why this is correct
High cardinality partition keys distribute writes evenly across partitions.
- ✗
Use a global secondary index (GSI) as the primary index for writes.
Why it's wrong here
GSIs are for querying, and they don't affect write distribution.
- ✗
Add a local secondary index (LSI) to the table.
Why it's wrong here
LSIs are for querying, not for improving write distribution.
Quick reference
AWS S3 Storage Class Comparison
| Storage Class | Min Duration | Retrieval | Use Case |
|---|---|---|---|
| S3 Standard | None | Immediate | Frequently accessed data |
| S3 Standard-IA | 30 days | Immediate | Infrequent access, rapid retrieval |
| S3 One Zone-IA | 30 days | Immediate | Non-critical infrequent data |
| S3 Intelligent-Tiering | None | Immediate–hours | Unknown or changing access patterns |
| S3 Glacier Instant | 90 days | Milliseconds | Archive with instant retrieval |
| S3 Glacier Flexible | 90 days | Minutes–hours | Archive, flexible retrieval |
| S3 Glacier Deep Archive | 180 days | Hours | Long-term compliance archive |
Go deeper
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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