Question 1mediummultiple choice
Full question →mediummultiple choiceObjective-mapped
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
Good practice is not just finding the correct option. The wrong answers often show the exact trap the exam wants you to fall into.
A
Distractor review
Increase the table's provisioned capacity (read/write units) to handle the promotion peak.
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
Best answer
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
Distractor review
Enable DAX caching for all reads but keep the same partition key and item layout.
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
Distractor review
Switch the table to eventually consistent reads for queries to lower read throttling.
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 trap
Common exam trap: NAT rules depend on direction and matching traffic
NAT is not only about the public address. The inside/outside interface roles and the ACL or rule that matches traffic are just as important.
Technical deep dive
How to think about this question
NAT questions usually test address translation, overload/PAT behaviour, static mappings and whether the right traffic is being translated. Read the interface direction and address terms carefully.
KKey Concepts to Remember
- Static NAT maps one inside address to one outside address.
- PAT allows many inside hosts to share one public address using ports.
- Inside local and inside global describe the private and translated addresses.
- NAT ACLs identify traffic for translation, not always security filtering.
TExam Day Tips
- Identify inside and outside interfaces first.
- Check whether the scenario needs static NAT, dynamic NAT or PAT.
- Do not confuse NAT matching ACLs with normal packet-filtering intent.
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FAQ
Questions learners often ask
What does this SAA-C03 question test?
Static NAT maps one inside address to one outside address.
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. — Because all events use the same partition key value (tenantId = "ACME"), DynamoDB concentrates throughput on a single partition key value and that partition can throttle even if the rest of the table has spare capacity. The most direct fix is to increase parallelism by sharding the partition key (for example, tenantId + shardId derived from a hash of eventId). This spreads requests across multiple partitions. Since the application can tolerate slight reordering across partitions, this design fits the stated constraints and improves overall throughput during promotions. A may reduce overall throttling but does not eliminate the single-partition bottleneck. C improves reads but does not redistribute writes, so hot-partition throttling can persist. D reduces read unit consumption but does not address write concentration on the single partition key value, so throughput can still be constrained at the partition level.
What should I do if I get this SAA-C03 question wrong?
Then try more questions from the same exam bank and focus on understanding why the wrong options are tempting.
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