DEA-C01 Data Ingestion and Transformation Practice Question
A company is using Amazon Kinesis Data Streams with a Lambda consumer to process clickstream data. The data rate is high and the Lambda function is falling behind, resulting in increased processing latency. What is the MOST effective way to improve throughput?
⚠ Common exam trap
The DEA-C01 exam often tests the misconception that Lambda performance tuning (memory/timeout) is the primary solution for stream processing backpressure, when in fact the shard count is the fundamental parallelism bottleneck in Kinesis Data Streams with a Lambda consumer.
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 number of shards in the Kinesis stream.
Increasing the number of shards in the Kinesis stream increases the stream's total read capacity, allowing more concurrent Lambda invocations to process records in parallel. Since each shard supports up to 5 read transactions per second and a maximum of 2 MB/s read throughput, adding shards directly raises the aggregate throughput, enabling the Lambda consumer to keep up with the high data rate.
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 memory allocated to the Lambda function.
Why it's wrong here
Memory scaling raises CPU share and network bandwidth per invocation, but Lambda polls each shard with a single concurrent batch, so per-shard throughput stays capped regardless of memory. It is tempting because memory tuning genuinely accelerates CPU-bound functions, yet the bottleneck here is shard-level parallelism, which only higher shard count or enhanced fan-out addresses.
- ✗
Increase the Lambda function timeout.
Why it's wrong here
Timeout extension only lets a slow invocation run longer before being killed; it neither raises records processed per second nor reduces the backlog, and can worsen latency by holding batches. It is tempting because timeouts do cause failures in lagging consumers, but the correct remedy for throughput is scaling shard count or parallelisation, not longer execution windows.
- ✗
Use Kinesis Data Firehose instead of Lambda.
Why it's wrong here
Firehose delivers records to destinations such as S3 or Redshift and cannot run your Lambda processing logic, so it removes the computation rather than scaling it. Raising the Lambda concurrency or shard count increases throughput. Firehose suits simple ingestion-to-storage pipelines, not custom stream processing.
- ✓
Increase the number of shards in the Kinesis stream.
Why this is correct
Adding shards raises the stream's parallel capacity, since each shard supports one Lambda invocation per batch and caps ingestion at 1 MB/s or 1,000 records/s. With the Lambda consumer throttled by shard-level concurrency, horizontal scaling of the stream directly relieves the backlog causing the latency.
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.