DEA-C01 Data Ingestion and Transformation Practice Question
A company needs to ingest real-time clickstream data from a web application into Amazon Redshift with minimal latency. The data volume is high and requires processing before loading. Which architecture is MOST appropriate?
⚠ Common exam trap
Many exam-takers confuse 'real-time' with 'near-real-time' and choose a batch option like Glue (A) or an indirect streaming path like S3 -> Lambda (B), failing to recognize that Kinesis Data Firehose is the only AWS service that natively integrates streaming ingestion with Redshift without additional latency or complexity.
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
✓
Kinesis Data Streams -> Kinesis Data Firehose -> Redshift
D is correct because Kinesis Data Streams captures high-volume clickstream data in real time, and Kinesis Data Firehose can buffer, transform (e.g., with Lambda), and load the data directly into Amazon Redshift with near-zero latency. This architecture is purpose-built for streaming ingestion with minimal overhead, unlike batch or intermediary storage approaches.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
AWS Glue ETL jobs scheduled every 5 minutes -> Redshift
Why it's wrong here
Glue is batch, not real-time.
- ✗
S3 -> Lambda -> Redshift
Why it's wrong here
S3 is not real-time; Lambda triggered by S3 events has latency.
- ✗
DynamoDB Streams -> Lambda -> Redshift
Why it's wrong here
DynamoDB Streams is for DynamoDB changes, not clickstream.
- ✓
Kinesis Data Streams -> Kinesis Data Firehose -> Redshift
Why this is correct
Provides real-time ingestion with transformation capability.
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.