Boosting Kinesis Stream Processing: Use Parallelization Factor in Lambda
A developer is troubleshooting an AWS Lambda function that processes records from an Amazon Kinesis Data Stream. The function is configured with a batch size of 100 and a parallelization factor of 1. The developer notices that the function is processing records slowly, and the iterator age is increasing. CloudWatch Logs show that the function is not experiencing errors or throttling, but the execution time per invocation is close to the 5-minute timeout. The stream has 10 shards. What is the most cost-effective way to increase processing throughput?
Quick Answer
The answer is to increase the parallelization factor to 10. This is the most cost-effective solution because it allows each of the 10 shards to be processed by up to 10 concurrent Lambda invocations, directly addressing the slow processing and rising iterator age without adding more shards or incurring additional stream costs. Since the function is not throttled or erroring, the bottleneck is the per-invocation execution time near the 5-minute timeout; parallelization lets multiple batches from the same shard run simultaneously, boosting throughput. On the AWS Certified Developer Associate DVA-C02 exam, this scenario tests your understanding of how Lambda’s parallelization factor decouples concurrency from shard count—a common trap is thinking you must increase shards or batch size, but the factor is a free lever. Remember: for a slow, non-throttled function, “parallelize the factor, not the shards.”
⚠ Common exam trap
Many exam-takers assume increasing shards is the only way to scale Kinesis processing, but the parallelization factor is a cost-effective Lambda-specific tuning knob that increases concurrency without additional shard costs.
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
✓
Increase the parallelization factor to 10
Increasing the parallelization factor to 10 allows each shard to be processed by up to 10 concurrent Lambda invocations, which directly increases throughput without additional shard costs. Since the function is not throttled or erroring, the bottleneck is the per-invocation processing time; parallelization reduces the iterator age by processing multiple batches per shard simultaneously.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Increase the batch size to 1000
Why it's wrong here
A larger batch size may increase execution time further, potentially causing the function to exceed the timeout. It does not address the per-record processing bottleneck.
- ✓
Increase the parallelization factor to 10
Why this is correct
The parallelization factor determines the number of concurrent Lambda invocations per shard. Increasing it allows multiple invocations to process records from the same shard simultaneously, dramatically increasing throughput without additional shard costs.
- ✗
Increase the memory of the Lambda function
Why it's wrong here
Increasing memory may improve performance if the function is CPU-bound, but it is not guaranteed and increases cost. The function is already near its timeout, indicating a potential bottleneck in record processing logic.
- ✗
Split the stream into more shards
Why it's wrong here
Splitting the stream increases the number of shards, leading to more Lambda invocations but also higher Kinesis costs. This is less cost-effective than increasing the parallelization factor.
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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Same concept, more angles
1 more way this is tested on DVA-C02
These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.
Variation 1. A developer is troubleshooting an AWS Lambda function that processes records from an Amazon Kinesis Data Stream. The function is configured with a batch size of 100 and a parallelization factor of 1. The iterator age metric is increasing, and CloudWatch Logs show the function execution time is around 4 minutes (timeout is 5 minutes). The stream has 10 shards. What is the most cost-effective way to increase processing throughput?
medium- A.Increase the batch size to 500
- B.Increase the number of shards
- C.Increase the timeout to 10 minutes
- ✓ D.Increase the parallelization factor per shard
Why D: Increasing the parallelization factor per shard (option D) allows each shard to be processed by multiple Lambda instances concurrently, which directly increases throughput without requiring additional shards or changes to the stream. Since the function is not hitting the 5-minute timeout but is taking ~4 minutes per batch, the bottleneck is processing concurrency per shard, not batch size or execution duration. This is the most cost-effective solution because it uses existing shards and avoids the cost of additional shards or unnecessary timeout increases.
JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This DVA-C02 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 DVA-C02 exam.