AWS DVA-C02 Drill: DynamoDB Provisioned Throughput - Handling Throttling with Backoff and Scaling

Jeff’s Note

#

Unlike generic exam dumps, ADH analyzes this scenario through the lens of a Real-World Lead Developer.

For DVA-C02 candidates, the confusion often lies in distinguishing between correcting API usage pitfalls and scaling practices under DynamoDB’s provisioned capacity model. In production, this is about knowing exactly how to handle ProvisionedThroughputExceededException via backoff strategies and capacity adjustments without blindly increasing retries or resources. Let’s drill down.

The Certification Drill (Simulated Question)

#

Scenario

#

A fintech startup, Apex Data Services, has built a microservice that inserts transactional data into an Amazon DynamoDB table configured with provisioned capacity. The microservice is deployed on a t4g.nano Amazon EC2 instance to minimize costs during early stages. However, the application logs reveal repeated failures with ProvisionedThroughputExceededException errors.

The Requirement:

#

Determine the best actions to resolve these throttling errors without unnecessary cost overhead or degraded performance.

The Options

#

• A) Move the microservice to a larger EC2 instance type.
• B) Increase the number of write capacity units (WCUs) provisioned for the DynamoDB table.
• C) Implement exponential backoff and jitter to reduce the frequency of retry requests to DynamoDB.
• D) Decrease the retry delay to increase the retry frequency and reduce latency.
• E) Change the capacity mode of the DynamoDB table from provisioned to on-demand.

Google adsense

#

leave a comment:

Correct Answer

#

B) Increase the number of write capacity units (WCUs) provisioned for the DynamoDB table.
C) Implement exponential backoff and jitter to reduce the frequency of retry requests to DynamoDB.

Quick Insight: The Developer Imperative

#

• DynamoDB’s ProvisionedThroughputExceededException means your provisioned throughput limit for reads or writes has been exceeded.
• Simply moving to a bigger EC2 instance (A) won’t help DynamoDB’s throttling.
• Increasing retries without backoff (D) can cause more throttling and higher latency.
• Switching to on-demand mode (E) could solve throttling but may be costlier depending on workload patterns.
• Best practice: scale provisioned capacity (B) and implement exponential backoff + jitter (C) in your client SDK.

Content Locked: The Expert Analysis

#

You’ve identified the answer. But do you know the implementation details that separate a Junior from a Senior?

The Expert’s Analysis

#

Correct Answer

#

Options B and C

The Winning Logic

#

• B) Increasing WCUs:
  Provisioned DynamoDB tables enforce strict throughput limits. When exceeded, DynamoDB throttles requests returning ProvisionedThroughputExceededException. Increasing provisioned write capacity aligns the table’s throughput with application demand, reducing throttling.

• C) Exponential Backoff and Jitter:
  Retrying immediately or frequently (like option D suggests) without backoff can exacerbate throttling by flooding DynamoDB with requests. Exponential backoff combined with jitter spreads retry attempts over time to avoid hot partitions and traffic spikes. The AWS SDKs have native support for this pattern.

This two-pronged approach ensures the application handles cloud-native limits gracefully while adapting capacity for workload changes.

The Trap (Distractor Analysis)

#

• A) Move to larger EC2 instance:
  Throughput errors come from DynamoDB limits, not EC2 compute power. Changing instance size doesn’t impact DynamoDB throttling.

• D) Decrease retry delay:
  Decreasing retry delay results in more retries hitting limits rapidly, increasing contention and latency — the opposite of best practices.

• E) Switch to on-demand:
  While on-demand capacity automatically scales and can prevent throttling, it may lead to unpredictable costs. The question emphasizes fixing throttling under provisioned capacity. On-demand is a valid option but not always preferred in cost-sensitive production.

The Technical Blueprint

#

# Using AWS CLI to increase write capacity units from 5 to 20 example:
aws dynamodb update-table \
    --table-name TransactionData \
    --provisioned-throughput ReadCapacityUnits=5,WriteCapacityUnits=20

# SDK snippet illustrating exponential backoff is typically built-in, but pseudocode:
retryCount = 0
while retryCount < maxRetries:
    try:
        dynamodb.put_item(...)
        break
    except ProvisionedThroughputExceededException:
        delay = random_between(0, 2^retryCount * baseDelay)
        sleep(delay)
        retryCount += 1

The Comparative Analysis

#

Real-World Application (Practitioner Insight)

#

Exam Rule

#

For the exam, always pick scaling provisioned throughput plus applying exponential backoff when you see ProvisionedThroughputExceededException with provisioned capacity tables.

Real World

#

In production, some teams migrate to on-demand capacity for bursty or unpredictable workloads to avoid managing capacity manually, trading off cost predictability for simplicity.

(CTA) Stop Guessing, Start Mastering

#

Disclaimer

This is a study note based on simulated scenarios for the AWS DVA-C02 exam.