- A
Introduce write sharding by adding a bounded random suffix to the hot tenant partition key and fan out reads across the shards.
Sharding spreads the hot tenant’s traffic across multiple partitions so DynamoDB is no longer forced to serve all writes through one physical partition. Querying across the shard set restores access to the tenant’s data while reducing throttling. This is the standard fix when a single partition key becomes a hot spot.
- B
Add DynamoDB Accelerator (DAX) in front of the table for the repeated status reads.
DAX can serve repeated eventually consistent reads from an in-memory cache with microsecond latency. Because the business accepts a few seconds of staleness, DAX is a strong fit for the repeated latest-status access pattern. It reduces read pressure on the table and improves response times for hot read paths.
- C
Keep the same key design and increase only the table’s provisioned RCUs and WCUs.
Why wrong: More capacity can help only if the workload is broadly distributed. A single hot partition key can still bottleneck even when table capacity is raised. This option does not change the skewed access pattern that is causing throttling on one partition.
- D
Replace the table reads with a Scan operation to distribute the load across all partitions.
Why wrong: A Scan reads many items and is far more expensive and slower than a targeted Query. It would increase latency and consume more capacity, not reduce it. Scans are not a solution for a hot key or for low-latency repeated reads.
- E
Move the table to another Availability Zone so the hot tenant uses a different storage node.
Why wrong: DynamoDB is a regional service and its partitioning behavior is not solved by picking a different AZ. The issue is logical data skew, not a single-AZ placement problem. Moving the table would not change the hot partition key pattern.
Quick Answer
The answer is to implement write sharding by appending a bounded random suffix to the partition key, which distributes the hot tenant’s writes across multiple partitions and prevents a single partition from throttling. This works because DynamoDB’s partition key determines physical storage; without sharding, all writes for one tenant land on the same partition, causing hot spots. By adding a random number (e.g., 1–10) to the tenantId, writes spread evenly, and reads fan out across all shards and aggregate results—acceptable here since the business tolerates a few seconds of staleness. On the SAA-C03 exam, this scenario tests your understanding of partition key design and throttling mitigation; a common trap is choosing a global secondary index instead, which doesn’t solve the write bottleneck. Pairing write sharding with DAX for repeated reads further reduces read latency. Memory tip: “Shard the hot key, fan out the reads.”
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 retail analytics table stores events in Amazon DynamoDB with partition key tenantId and sort key eventTime. During a promotion, one tenant generates most writes and repeatedly polls the same latest-status items, causing throttling on a single partition key and high latency on reads. The business can tolerate read results that are a few seconds stale. Which two changes will most effectively reduce throttling and latency? Select two.
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 write sharding by adding a bounded random suffix to the hot tenant partition key and fan out reads across the shards.
Option A is correct because write sharding distributes the hot tenant's writes across multiple partitions by appending a bounded random suffix to the partition key, preventing a single partition from throttling. Reads then fan out across all shards and aggregate results, which is acceptable since the business tolerates a few seconds of staleness. This directly addresses the single-partition bottleneck without changing the overall data model.
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 write sharding by adding a bounded random suffix to the hot tenant partition key and fan out reads across the shards.
Why this is correct
Sharding spreads the hot tenant’s traffic across multiple partitions so DynamoDB is no longer forced to serve all writes through one physical partition. Querying across the shard set restores access to the tenant’s data while reducing throttling. This is the standard fix when a single partition key becomes a hot spot.
Related concept
Read the scenario before looking for a memorised answer.
- ✓
Add DynamoDB Accelerator (DAX) in front of the table for the repeated status reads.
Why this is correct
DAX can serve repeated eventually consistent reads from an in-memory cache with microsecond latency. Because the business accepts a few seconds of staleness, DAX is a strong fit for the repeated latest-status access pattern. It reduces read pressure on the table and improves response times for hot read paths.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Keep the same key design and increase only the table’s provisioned RCUs and WCUs.
Why it's wrong here
More capacity can help only if the workload is broadly distributed. A single hot partition key can still bottleneck even when table capacity is raised. This option does not change the skewed access pattern that is causing throttling on one partition.
- ✗
Replace the table reads with a Scan operation to distribute the load across all partitions.
Why it's wrong here
A Scan reads many items and is far more expensive and slower than a targeted Query. It would increase latency and consume more capacity, not reduce it. Scans are not a solution for a hot key or for low-latency repeated reads.
- ✗
Move the table to another Availability Zone so the hot tenant uses a different storage node.
Why it's wrong here
DynamoDB is a regional service and its partitioning behavior is not solved by picking a different AZ. The issue is logical data skew, not a single-AZ placement problem. Moving the table would not change the hot partition key pattern.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often assume DAX alone can fix both read and write throttling, but DAX only caches reads and does not address the write-side partition bottleneck that causes throttling in the first place.
Detailed technical explanation
How to think about this question
Write sharding works by adding a random suffix (e.g., 0–N) to the partition key, so writes for the same tenant are spread across N physical partitions, each with its own throughput capacity. Reads must query all shards and merge results, which is efficient only if the application can tolerate eventual consistency or a few seconds of staleness. DynamoDB's partition throughput limit is 3000 RCUs or 1000 WCUs per partition, so sharding effectively multiplies the available capacity for the hot tenant.
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: Introduce write sharding by adding a bounded random suffix to the hot tenant partition key and fan out reads across the shards. — Option A is correct because write sharding distributes the hot tenant's writes across multiple partitions by appending a bounded random suffix to the partition key, preventing a single partition from throttling. Reads then fan out across all shards and aggregate results, which is acceptable since the business tolerates a few seconds of staleness. This directly addresses the single-partition bottleneck without changing the overall data model.
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
This SAA-C03 practice question is part of Courseiva's free Amazon Web Services certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the SAA-C03 exam.
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