DEA-C01 Data Operations and Support Practice Question
A company uses Amazon Kinesis Data Streams to ingest clickstream data. The data is consumed by an AWS Lambda function that processes each record and writes to an Amazon DynamoDB table. Recently, the Lambda function has been failing with 'ProvisionedThroughputExceededException' from DynamoDB. The Lambda function uses the AWS SDK to batch write items in batches of 25. The DynamoDB table has on-demand capacity mode. The stream has 10 shards, and the Lambda function is configured with a batch size of 100 and 5 concurrent invocations per shard. What step should the team take to resolve the issue?
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
✓
Reduce the Lambda batch size to 25 and implement exponential backoff with jitter in the Lambda code.
The issue is DynamoDB throttling due to high write traffic from Lambda. The DynamoDB table is on-demand, which can throttle if bursts exceed sustained limits. Reducing the Lambda batch size from 100 to 25 decreases the number of records processed per invocation, lowering the instantaneous write rate. Implementing exponential backoff with jitter in the Lambda code allows retries on throttled requests, making the system more resilient. Option A is not required because on-demand mode automatically scales, and switching to provisioned can be costly. Option C is incorrect because increasing shards would increase parallelism and worsen throttling. Option D is incorrect because increasing concurrency would also increase write pressure on DynamoDB.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Switch the DynamoDB table from on-demand to provisioned capacity with a high write capacity unit (WCU) value.
Why it's wrong here
Switching to provisioned capacity is unnecessary; on-demand mode can handle the load if throttling is managed properly. This does not address the root cause.
- ✓
Reduce the Lambda batch size to 25 and implement exponential backoff with jitter in the Lambda code.
Why this is correct
Reducing batch size and adding exponential backoff directly reduces the write rate and adds retry logic, mitigating throttling. This is the best approach.
- ✗
Increase the number of Kinesis shards to 20 to reduce the load per shard.
Why it's wrong here
Increasing shards would increase the number of concurrent Lambda invocations, further increasing write pressure on DynamoDB.
- ✗
Increase the Lambda function's reserved concurrency to allow more parallel executions.
Why it's wrong here
Increasing reserved concurrency would allow more parallel executions, worsening the throttling issue.
Visual reference
Quick reference
Cloud Service Model Comparison
| Model | You Manage | Provider Manages | Examples |
|---|---|---|---|
| IaaS | OS, runtime, apps, data | Hardware, hypervisor, networking | EC2, Azure VMs, GCP Compute Engine |
| PaaS | Apps and data | OS, runtime, middleware, hardware | Elastic Beanstalk, Azure App Service |
| SaaS | Data and settings only | Everything else | Microsoft 365, Salesforce, Workday |
| FaaS / Serverless | Function code only | Infra, scaling, runtime | Lambda, Azure Functions, Cloud Run |
| CaaS | Containers and apps | Kubernetes, OS, hardware | EKS, AKS, GKE |
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This DEA-C01 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 DEA-C01 exam.