SAA-C03 Design High-Performing Architectures Practice Question
A DynamoDB table for a retail API has a partition key based only on the current date. Write throttling occurs during business hours. What is the best design change? The architecture review board prefers a managed AWS-native control.
⚠ Common exam trap
A common mix-up: candidates think adding a GSI or adjusting capacity solves throttling, but the root cause is the partition key's low cardinality, which only a higher-cardinality key can fix by distributing writes across partitions.
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 higher-cardinality partition key that distributes writes across partitions
Using only the current date as a partition key creates a hot partition because all writes for the day target a single partition, leading to throttling. A higher-cardinality partition key, such as a composite key combining date with a unique attribute like user ID or order ID, distributes writes evenly across multiple partitions, fully utilizing DynamoDB's provisioned throughput. This is the best managed-native solution to resolve write throttling without changing the table's capacity or moving data.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Use a higher-cardinality partition key that distributes writes across partitions
Why this is correct
With only three possible partition key values, all writes for a given value are routed to the same partition, which is hard-capped at 1,000 write capacity units per second. A higher-cardinality partition key, such as a composite key combining customer ID with a timestamp, spreads write traffic across many partitions and uses the table's provisioned throughput more effectively, preventing hot-partition throttling.
- ✗
Create a global secondary index with the same date key
Why it's wrong here
A global secondary index with the same date key inherits the exact same hot-partition problem. Each GSI has its own partition limits, and because the base table's hot date value also appears as the GSI partition key, every write to the base table generates a synchronous write to a single index partition, causing the index to throttle even if the base table is redesigned.
- ✗
Reduce the table's write capacity
Why it's wrong here
Reducing the table's write capacity directly lowers the number of write request units available per second, so the existing workload will more frequently exceed the provisioned limit and trigger throttling. The root cause is uneven traffic concentrating on a low-cardinality key, not an overall surplus of capacity, so lowering capacity worsens contention rather than resolving it.
- ✗
Move the table to S3 Glacier Instant Retrieval
Why it's wrong here
Moving the table to S3 Glacier Instant Retrieval is a category error: DynamoDB is a low-latency NoSQL database designed for item-level reads and writes, while S3 Glacier Instant Retrieval is object storage for infrequently accessed data with retrieval latency measured in milliseconds but no item-level update or query semantics. It cannot serve the API's write workload, and object storage has no partition-aware write scaling that addresses the hot-key problem.
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