- A
Keep tenantId as the partition key and rely on DynamoDB adaptive capacity to automatically remove all throttling.
Why wrong: Adaptive capacity may help with some workloads, but it cannot eliminate the fundamental issue when a single partition key (tenantId) receives sustained traffic beyond that partition’s throughput. For a hot tenant, you still need to distribute writes across multiple physical partitions.
- B
Add a shard attribute to the partition key (partition key = tenantId#shard, where shard is randomly selected from a fixed range). Query all shards for the tenant for eventTime values in the last 10 minutes, then merge results in the application.
This “write sharding” spreads a tenant’s traffic across multiple partition key values, which distributes the write load across multiple DynamoDB partitions (and thus multiple throughput slices). Reads for the last 10 minutes remain efficient because each shard still supports a sort-key range query on eventTime; the application merges results across shards.
- C
Change the sort key to eventTimeBucket (for example, eventTime rounded to 1-minute buckets) while keeping the partition key as tenantId.
Why wrong: Changing only the sort key does not resolve hot partition issues because the partition key (tenantId) still concentrates all writes for that tenant into a single partition. DynamoDB distributes capacity primarily by partition key, not sort key.
- D
Enable DAX and use it for write operations so throttled writes are served from cache instead of reaching DynamoDB.
Why wrong: DAX is a read-through cache designed to accelerate reads, not to absorb write throughput or bypass DynamoDB write capacity limits. Throttling on writes occurs before any cached read path, so DAX does not address the hot-partition write bottleneck.
SAA-C03 Design High-Performing Architectures Practice Question
This SAA-C03 practice question tests your understanding of design high-performing architectures. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
An event ingestion service writes to a DynamoDB table where the partition key is tenantId and the sort key is eventTime. During a campaign, one tenant generates a disproportionate share of traffic, causing write throttling and increased latency for that tenant’s writes. You can change the data model and application queries, but you must still efficiently retrieve events for a tenant for the last 10 minutes. Which change best improves write throughput by reducing hot 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
Add a shard attribute to the partition key (partition key = tenantId#shard, where shard is randomly selected from a fixed range). Query all shards for the tenant for eventTime values in the last 10 minutes, then merge results in the application.
Option B is correct because it distributes writes for a hot tenant across multiple partitions by appending a random shard suffix to the tenantId partition key. This eliminates a single hot partition, allowing DynamoDB to scale write capacity horizontally. The application can then query all shards for the last 10 minutes and merge results, satisfying the retrieval requirement.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Keep tenantId as the partition key and rely on DynamoDB adaptive capacity to automatically remove all throttling.
Why it's wrong here
Adaptive capacity may help with some workloads, but it cannot eliminate the fundamental issue when a single partition key (tenantId) receives sustained traffic beyond that partition’s throughput. For a hot tenant, you still need to distribute writes across multiple physical partitions.
- ✓
Add a shard attribute to the partition key (partition key = tenantId#shard, where shard is randomly selected from a fixed range). Query all shards for the tenant for eventTime values in the last 10 minutes, then merge results in the application.
Why this is correct
This “write sharding” spreads a tenant’s traffic across multiple partition key values, which distributes the write load across multiple DynamoDB partitions (and thus multiple throughput slices). Reads for the last 10 minutes remain efficient because each shard still supports a sort-key range query on eventTime; the application merges results across shards.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Change the sort key to eventTimeBucket (for example, eventTime rounded to 1-minute buckets) while keeping the partition key as tenantId.
Why it's wrong here
Changing only the sort key does not resolve hot partition issues because the partition key (tenantId) still concentrates all writes for that tenant into a single partition. DynamoDB distributes capacity primarily by partition key, not sort key.
- ✗
Enable DAX and use it for write operations so throttled writes are served from cache instead of reaching DynamoDB.
Why it's wrong here
DAX is a read-through cache designed to accelerate reads, not to absorb write throughput or bypass DynamoDB write capacity limits. Throttling on writes occurs before any cached read path, so DAX does not address the hot-partition write bottleneck.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates assume adaptive capacity or caching (DAX) can solve write throttling, but neither addresses the root cause—a single partition exceeding its write capacity—which requires redistributing the partition key across multiple physical partitions.
Detailed technical explanation
How to think about this question
Under the hood, DynamoDB partitions data by partition key hash; a single partition can handle up to 1,000 write capacity units (WCU) and 3,000 read capacity units (RCU). By using a composite partition key like tenantId#shard with a random shard value (e.g., 0–9), writes are spread across up to 10 partitions, multiplying the effective write throughput for that tenant. The application must perform a scatter-gather query across all shards for the last 10 minutes, which is efficient because each shard query uses the sort key eventTime to filter recent data.
KKey Concepts to Remember
- Read the scenario before looking for a memorised answer.
- Find the constraint that changes the correct option.
- Eliminate answers that are true in general but not in this case.
TExam Day Tips
- Watch for words such as best, first, most likely and least administrative effort.
- Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
A media company stores terabytes of video archives that are accessed once a year for audit purposes. Moving these objects to a cold storage tier (Azure Archive, S3 Glacier, or Google Nearline) costs a fraction of hot storage. Questions like this test whether you understand storage tiers, access frequency tradeoffs, and retrieval latency requirements.
What to study next
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FAQ
Questions learners often ask
What does this SAA-C03 question test?
Design High-Performing Architectures — This question tests Design High-Performing Architectures — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Add a shard attribute to the partition key (partition key = tenantId#shard, where shard is randomly selected from a fixed range). Query all shards for the tenant for eventTime values in the last 10 minutes, then merge results in the application. — Option B is correct because it distributes writes for a hot tenant across multiple partitions by appending a random shard suffix to the tenantId partition key. This eliminates a single hot partition, allowing DynamoDB to scale write capacity horizontally. The application can then query all shards for the last 10 minutes and merge results, satisfying the retrieval requirement.
What should I do if I get this SAA-C03 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 11, 2026
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