- A
Increase the table's provisioned capacity (read/write units) to handle the promotion peak.
Why wrong: More overall capacity can help with table-wide limits, but hot-partition throttling is caused by one physical partition becoming saturated by a single partition key value. Even with higher total table capacity, the tenantId = "ACME" partition can still hit its per-partition limits and continue throttling. It also tends to be costly for temporary spikes.
- B
Change the partition key to include an additional sharding attribute derived from a hash of eventId.
When all traffic targets one partition key value, that partition becomes the bottleneck regardless of total table capacity. Adding a shard/salt attribute to the partition key (for example, tenantId + shardId where shardId = hash(eventId) mod N) spreads writes across multiple partition key values, increasing partition-level parallelism. Because the scenario allows slight reordering across partitions, losing strict single-partition time ordering is acceptable while improving throughput and reducing throttling.
- C
Enable DAX caching for all reads but keep the same partition key and item layout.
Why wrong: DAX can reduce read latency and help offload repeat reads from DynamoDB, but it does not change where writes land. During a promotion, throttling is typically driven by heavy write demand as well, so the same hot partition remains a write hotspot. DAX effectiveness depends on having a high degree of repeated reads to the same keys.
- D
Switch the table to eventually consistent reads for queries to lower read throttling.
Why wrong: Eventually consistent reads reduce read request unit consumption for reads, which can help if throttling is purely read-related. However, it does not address write hot-partition throttling, and the scenario states throttling during a promotion where write and read load may both be high. Also, it does not increase partition-level parallelism.
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.
A DynamoDB-backed event processing system experiences throttling during a promotion. All events are written and read using the same partition key value (tenantId = "ACME"). The workload is time-ordered per tenant, and the application can tolerate slight reordering across partitions. Which design change will most directly increase throughput and reduce hot-partition throttling?
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
Change the partition key to include an additional sharding attribute derived from a hash of eventId.
Option B is correct because adding a sharding attribute derived from a hash of eventId allows writes and reads to be distributed across multiple partition keys, breaking the single hot partition caused by using tenantId='ACME' for all operations. DynamoDB's throughput is limited per partition, so distributing the load across many partitions directly reduces throttling without changing the application's tolerance for slight reordering.
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.
- ✗
Increase the table's provisioned capacity (read/write units) to handle the promotion peak.
Why it's wrong here
More overall capacity can help with table-wide limits, but hot-partition throttling is caused by one physical partition becoming saturated by a single partition key value. Even with higher total table capacity, the tenantId = "ACME" partition can still hit its per-partition limits and continue throttling. It also tends to be costly for temporary spikes.
- ✓
Change the partition key to include an additional sharding attribute derived from a hash of eventId.
Why this is correct
When all traffic targets one partition key value, that partition becomes the bottleneck regardless of total table capacity. Adding a shard/salt attribute to the partition key (for example, tenantId + shardId where shardId = hash(eventId) mod N) spreads writes across multiple partition key values, increasing partition-level parallelism. Because the scenario allows slight reordering across partitions, losing strict single-partition time ordering is acceptable while improving throughput and reducing throttling.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Enable DAX caching for all reads but keep the same partition key and item layout.
Why it's wrong here
DAX can reduce read latency and help offload repeat reads from DynamoDB, but it does not change where writes land. During a promotion, throttling is typically driven by heavy write demand as well, so the same hot partition remains a write hotspot. DAX effectiveness depends on having a high degree of repeated reads to the same keys.
- ✗
Switch the table to eventually consistent reads for queries to lower read throttling.
Why it's wrong here
Eventually consistent reads reduce read request unit consumption for reads, which can help if throttling is purely read-related. However, it does not address write hot-partition throttling, and the scenario states throttling during a promotion where write and read load may both be high. Also, it does not increase partition-level parallelism.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often assume increasing provisioned capacity (Option A) is the universal fix for throttling, but AWS specifically tests the understanding that DynamoDB's per-partition throughput limits require a sharding strategy to distribute load across partitions.
Trap categories for this question
Scenario analysis trap
Eventually consistent reads reduce read request unit consumption for reads, which can help if throttling is purely read-related. However, it does not address write hot-partition throttling, and the scenario states throttling during a promotion where write and read load may both be high. Also, it does not increase partition-level parallelism.
Detailed technical explanation
How to think about this question
DynamoDB partitions data by partition key hash; each partition can handle up to 3000 RCU and 1000 WCU. By using a composite key like 'tenantId#shardId' (e.g., 'ACME#0' to 'ACME#9'), the workload is spread across 10 partitions, increasing aggregate throughput to 10,000 WCU. The application's tolerance for slight reordering across partitions is key—this design works because events are time-ordered per shard, not globally, and the application can merge results later.
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 cloud solutions architect for a retail company is evaluating services for a new workload. The correct answer here reflects best practice for the specific scenario described — not a general cloud recommendation. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Cloud exam questions reward reading the constraint carefully: the same technology can be right or wrong depending on the use case.
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: Change the partition key to include an additional sharding attribute derived from a hash of eventId. — Option B is correct because adding a sharding attribute derived from a hash of eventId allows writes and reads to be distributed across multiple partition keys, breaking the single hot partition caused by using tenantId='ACME' for all operations. DynamoDB's throughput is limited per partition, so distributing the load across many partitions directly reduces throttling without changing the application's tolerance for slight reordering.
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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