- A
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.
- B
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.
- C
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.
- D
Increase only the sort-key cardinality while leaving the partition key unchanged.
Why wrong: 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.
- E
Move the table to the Standard-IA table class because throttling is usually caused by storage class selection.
Why wrong: 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.
Quick Answer
The answer is to implement DynamoDB DAX in front of the table for the repeated dashboard reads. This is correct because the hot partition caused by a single tenant’s high write volume is best addressed by sharding the partition key—for example, appending a random shardId to tenantId—which distributes writes across multiple physical partitions and eliminates throttling. DAX then absorbs the repeated, slightly stale read queries for recent items, offloading the table and improving responsiveness without requiring strong consistency. On the SAA-C03 exam, this scenario tests your understanding of DynamoDB’s partition limits and the read/write capacity model, with a common trap being to over-provision RCU/WCU instead of using sharding and caching. A key memory tip: “Shard the hot key, cache the hot reads” to remember that sharding fixes write throttling while DAX handles read-heavy dashboards.
SAA-C03 Design High-Performing Architectures Practice Question
This SAA-C03 practice question tests your understanding of design high-performing architectures. The scenario asks you to isolate a root cause — eliminate options that address a different problem before choosing. 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 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
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
Introduce a sharded partition key for the hot tenant and query the small shard set when reading recent data.
Option A is correct because introducing a sharded partition key (e.g., tenantId#shardId) for the hot tenant spreads its write traffic across multiple physical partitions, reducing throttling. When querying recent data, the dashboard can read from a small, fixed set of shards (e.g., 10 shards) and merge results, which is efficient and tolerates the slight staleness. This pattern directly addresses the hot partition issue without requiring application-level aggregation.
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.
- ✓
Introduce a sharded partition key for the hot tenant and query the small shard set when reading recent data.
Why this is correct
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.
Related concept
Read the scenario before looking for a memorised answer.
- ✓
Add a time bucket to the partition key, such as tenantId#YYYYMMDDHH, to spread bursty writes across more partitions.
Why this is correct
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.
Related concept
Read the scenario before looking for a memorised answer.
- ✓
Place DynamoDB DAX in front of the table for the repeated dashboard reads of recent items.
Why this is correct
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.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Increase only the sort-key cardinality while leaving the partition key unchanged.
Why it's wrong here
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.
- ✗
Move the table to the Standard-IA table class because throttling is usually caused by storage class selection.
Why it's wrong here
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 traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates assume increasing sort-key cardinality (Option D) improves write throughput, but DynamoDB's partition key alone determines the physical partition, so only modifying the partition key or using write sharding can alleviate hot partition throttling.
Detailed technical explanation
How to think about this question
DynamoDB distributes items across partitions based on the partition key's hash value; a single tenantId maps to one partition, so 10x traffic saturates that partition's 1000 WCU (on-demand) or provisioned limit. Adding a time-bucket suffix (tenantId#YYYYMMDDHH) spreads writes across multiple partitions per hour, but queries for recent data must scan all buckets; DAX caches frequent reads, reducing RCU consumption and latency, but does not solve write throttling. Sharding with a fixed shard count (e.g., 10) ensures even write distribution while allowing efficient reads by querying all shards in parallel.
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
Got this wrong? Here's your next step.
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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: Introduce a sharded partition key for the hot tenant and query the small shard set when reading recent data. — Option A is correct because introducing a sharded partition key (e.g., tenantId#shardId) for the hot tenant spreads its write traffic across multiple physical partitions, reducing throttling. When querying recent data, the dashboard can read from a small, fixed set of shards (e.g., 10 shards) and merge results, which is efficient and tolerates the slight staleness. This pattern directly addresses the hot partition issue without requiring application-level aggregation.
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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