SAA-C03 Design High-Performing Architectures Practice Question
A DynamoDB-backed multi-tenant app experiences throttling during a promotion. Most writes and reads target tenant "ACME" and use the same partition key value, causing a hot partition. Which design change most directly improves performance?
⚠ Common exam trap
Many candidates think increasing total table capacity (Option B) solves throttling, but they overlook that DynamoDB throttles at the partition level, not the table level, so a single hot partition remains constrained regardless of total capacity.
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
✓
Add a "shard" component to the partition key (for example, tenantId + hashed bucket) to spread traffic across partitions
Adding a shard component to the partition key (e.g., appending a random or hash-based suffix to the tenant ID) distributes writes and reads for the same tenant across multiple physical partitions. This directly alleviates the hot partition caused by all ACME traffic hitting a single partition key value, allowing DynamoDB to utilize its full provisioned throughput across partitions.
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 a "shard" component to the partition key (for example, tenantId + hashed bucket) to spread traffic across partitions
Why this is correct
DynamoDB throughput is distributed across physical partitions. If one partition key value receives most traffic, that partition throttles. Adding a shard component to the partition key increases the number of partition key values being used, spreading requests across more partitions and reducing hot-partition throttling.
- ✗
Increase the table’s read capacity without changing the partition key
Why it's wrong here
Raising the table's provisioned read capacity does not address the root cause of hot-partition throttling. DynamoDB allocates throughput across physical partitions, and each partition has its own maximum capacity; a single heavily accessed partition key value can still saturate that partition's limits even if the table has abundant global capacity. This leaves other partitions underutilized while requests to the hot tenant continue to be throttled, so capacity increases are ineffective without first distributing the workload across more partition key values.
- ✗
Switch all reads to strongly consistent reads to guarantee faster results
Why it's wrong here
Switching to strongly consistent reads does not alter how DynamoDB routes requests or which physical partition stores the hot tenant's items. Strongly consistent reads actually consume twice the read capacity of eventually consistent reads and often have higher latency, and because they still target the same partition key value, they can worsen the existing throttling. This option confuses data consistency with load distribution and ignores the fact that the throttling is caused by partition-level hot spots, not by consistency mode.
- ✗
Store ACME data in S3 and query it directly to avoid DynamoDB throttling
Why it's wrong here
Moving the tenant's data to Amazon S3 and querying it directly replaces a purpose-built, low-latency NoSQL database with an object store that is not designed for high-rate, sub-second point lookups in an interactive API workload. S3 queries require either downloading full objects or using S3 Select, both of which add significant latency and lack the fine-grained item access and throughput control of DynamoDB; you would still need an index or store for metadata and would likely introduce new complexity such as Athena or additional services. This approach trades a solvable partition-key issue for a fundamentally different data access architecture that is not a drop-in substitute.
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