DEA-C01 Data Ingestion and Transformation Practice Question
A data engineer is ingesting streaming data from an IoT fleet into Amazon Kinesis Data Streams. The data must be transformed in real-time and loaded into an Amazon Redshift cluster. Which solution minimizes operational overhead?
⚠ Common exam trap
Watch out — candidates often confuse Kinesis Data Streams with Kinesis Data Firehose, assuming they must write a custom consumer (KCL) to transform data, when Firehose with Lambda provides a fully managed, serverless alternative that reduces operational overhead.
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
✓
Use Kinesis Data Firehose with a Lambda transformation function
Kinesis Data Firehose is the fully managed service for loading streaming data into Redshift with near-real-time latency. By attaching a Lambda transformation function, you can perform lightweight data transformations (e.g., JSON flattening, field masking) without managing any compute infrastructure. This combination eliminates the need to provision or tune any servers, clusters, or consumer applications, minimizing operational overhead.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Use Kinesis Data Firehose with a Lambda transformation function
Why this is correct
Firehose handles buffering, transformation via Lambda, and direct delivery to Redshift.
- ✗
Use AWS Glue ETL jobs running continuously
Why it's wrong here
Glue is designed for batch processing, not real-time streams.
- ✗
Use Kinesis Client Library (KCL) to consume and transform data, then write to Redshift using COPY
Why it's wrong here
KCL requires managing a consumer application, increasing operational overhead.
- ✗
Use AWS Direct Connect to stream data directly into Redshift
Why it's wrong here
Direct Connect provides network connectivity, not data transformation.
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 DEA-C01
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 data engineer is ingesting streaming data from an IoT fleet into Amazon S3 using Amazon Kinesis Data Firehose. The data arrives as JSON, but the downstream analytics require Parquet format. Which Firehose transformation should the engineer configure?
easy- A.Use an S3 lifecycle policy to convert JSON to Parquet.
- ✓ B.Configure a Lambda function as a data transformation in Firehose to convert JSON to Parquet.
- C.Use S3 Batch Operations to convert existing JSON objects to Parquet.
- D.Use Kinesis Data Analytics to convert the stream to Parquet before writing to S3.
Why B: Amazon Kinesis Data Firehose can invoke an AWS Lambda function as a data transformation step to convert incoming JSON records to Parquet format before delivery to S3. This is the native, serverless way to perform record-level format conversion within the Firehose delivery stream, ensuring downstream analytics tools can directly query the Parquet data without additional processing.
JA
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.