An event-ingestion application writes telemetry to DynamoDB with partition key tenantId and sort key eventTime. During a promotion, one tenant generates 10 times the normal traffic. Dashboards repeatedly query the most recent items for that tenant, and they can tolerate slightly stale data. Which changes would most effectively reduce throttling and improve responsiveness? Select three.
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.
Best answer
Introduce a sharded partition key for the hot tenant and query the small shard set when reading recent data.
Correct because hot-partition problems are usually solved by spreading traffic across multiple partition key values. Sharding ACME across several keys distributes write load and avoids a single overloaded partition.
Best answer
Add a time bucket to the partition key, such as tenantId#YYYYMMDDHH, to spread bursty writes across more partitions.
Correct because time bucketing naturally disperses write pressure during spikes. It keeps recent data queryable while preventing one tenant and one time range from concentrating all traffic on one partition.
Best answer
Place DynamoDB DAX in front of the table for the repeated dashboard reads of recent items.
Correct because DAX reduces read latency for repeated access patterns and can absorb many identical dashboard lookups. It is especially useful when slight staleness is acceptable.
Distractor review
Increase only the sort-key cardinality while leaving the partition key unchanged.
Incorrect because DynamoDB partitions primarily on the partition key. More sort-key variation does not distribute traffic across partitions when the same hot partition key still receives the load.
Distractor review
Move the table to the Standard-IA table class because throttling is usually caused by storage class selection.
Incorrect because table class affects storage cost and some access costs, not hot-partition distribution. It does not relieve a tenant-specific throughput bottleneck caused by an overloaded partition key.
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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Question 2
A team wants to run containerized services with AWS-managed orchestration and autoscaling. They do NOT require Kubernetes compatibility. Which AWS service choice is most appropriate to meet these goals?
Question 3
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Question 5
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Question 6
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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: Introduce a sharded partition key for the hot tenant and query the small shard set when reading recent data. — To fix this pattern, the table design must spread the hot tenant across more partitions and reduce repeated read pressure. Sharding and time bucketing distribute writes so one partition is not overwhelmed. DAX then absorbs repeated dashboard reads for the same recent items, which lowers latency and removes some pressure from DynamoDB. These are the most direct high-performance improvements for this workload. Increasing sort-key variation alone does not solve a partition-key hotspot, and changing table class does not address throughput distribution. The issue is skewed access to one tenant, so the architecture must change how data is keyed and cached.
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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