DVA-C02 Troubleshooting and Optimization Practice Question
A company's DynamoDB table has a read capacity of 10,000 RCUs and receives consistent traffic. Recently, users have reported increased latency for read requests. The application uses strongly consistent reads. The developer checks CloudWatch metrics and sees that 'ConsumedReadCapacityUnits' is at 9,500 but 'ThrottledRequests' is high. What is the most likely cause?
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
✓
A hot partition is exceeding its partition-level read capacity.
The correct answer is B: a hot partition is exceeding its partition-level read capacity. Even though the table's total consumed read capacity (9,500 of 10,000 RCUs) is below the provisioned limit, DynamoDB distributes capacity across partitions, and a single partition can only support a maximum of 3,000 RCUs (or 1,000 WCUs); if one partition key receives disproportionate traffic, that partition throttles requests while overall table capacity remains underutilized, which matches the high ThrottledRequests with consumed capacity below the table maximum. Option D is wrong because the table-level provisioned capacity is not exhausted (9,500 < 10,000), so low capacity is not the cause. Option A is wrong because the scenario states the application uses strongly consistent reads, and switching consistency would not explain throttling. Option C is wrong because auto scaling scaling down would reduce provisioned capacity, but the metric shows consumed capacity still below the provisioned 10,000 RCUs, and aggressive scale-down is not the typical cause of partition-level throttling.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
The application is using eventually consistent reads but expecting strongly consistent results.
Why it's wrong here
Using eventually consistent reads when strongly consistent results are expected would lead to stale data, not throttling. Throttling is a direct consequence of exceeding provisioned capacity, whereas consistency models relate to data freshness guarantees. In fact, eventually consistent reads consume half the RCU of strongly consistent reads, making them more efficient and less likely to cause capacity issues due to insufficient RCU.
- ✓
A hot partition is exceeding its partition-level read capacity.
Why this is correct
DynamoDB distributes provisioned capacity evenly across its underlying partitions. If a specific partition key receives a disproportionately high volume of read requests, it can exhaust its allocated share of the table's total read capacity, even if the overall table capacity is not fully utilized. This scenario, known as a 'hot partition,' causes throttling errors for requests targeting that specific partition, despite ample table-level RCUs.
- ✗
The DynamoDB table has auto scaling enabled and is scaling down too aggressively.
Why it's wrong here
DynamoDB Auto Scaling dynamically adjusts provisioned capacity to maintain a target utilization, typically scaling up when demand increases and scaling down when it decreases. If auto scaling were scaling down too aggressively, it would reduce the *overall* table's provisioned capacity, leading to widespread throttling across the table. However, the problem states 10,000 RCU provisioned and 8,000 RCU consumed, indicating sufficient table-level capacity, making aggressive scaling down an unlikely cause for localized throttling.
- ✗
The provisioned read capacity is too low for the traffic.
Why it's wrong here
The problem explicitly states the DynamoDB table has a provisioned read capacity of 10,000 RCU and is consuming 8,000 RCU. Since the consumed capacity (8,000 RCU) is below the provisioned capacity (10,000 RCU), the table-level capacity is not being exceeded. Therefore, the issue is not a general lack of provisioned read capacity for the entire table, but rather a more localized or specific capacity constraint.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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