DEA-C01 Data Ingestion and Transformation Practice Question
A streaming application sends data to Amazon Kinesis Data Streams. The data must be enriched with reference data from an Amazon DynamoDB table in real-time. Which AWS service can be used to perform this enrichment with minimal latency?
⚠ Common exam trap
The DEA-C01 exam often tests the distinction between real-time stream processing (Kinesis Data Analytics for Flink) and near-real-time or batch-oriented services (Firehose, Glue ETL), leading candidates to choose Lambda because they assume serverless functions are always the lowest-latency option, ignoring concurrency and cold-start limitations in streaming contexts.
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
✓
Amazon Kinesis Data Analytics for Apache Flink
Amazon Kinesis Data Analytics for Apache Flink is correct because it allows you to run Apache Flink applications that can read from a Kinesis data stream, perform stateful stream processing, and enrich records in real-time by joining with reference data stored in DynamoDB. Flink's asynchronous I/O and managed state enable sub-second enrichment latency without the cold-start delays or concurrency limits of Lambda-based 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.
- ✓
Amazon Kinesis Data Analytics for Apache Flink
Why this is correct
Amazon Kinesis Data Analytics for Apache Flink runs continuous SQL or Flink applications directly over the stream, joining each record against the DynamoDB reference table in-flight. This satisfies the real-time enrichment requirement with minimal latency, avoiding the batching delay of Lambda-based or Firehose-based approaches.
- ✗
Amazon Kinesis Data Firehose with Lambda transformation
Why it's wrong here
Firehose buffers records before delivery and its Lambda transformation runs on batched payloads, so enrichment is not per-record real-time. Firehose suits loading transformed data into S3, Redshift or OpenSearch, not low-latency enrichment of an existing Kinesis Data Streams consumer.
- ✗
AWS Lambda function triggered by Kinesis Data Streams
Why it's wrong here
A Lambda function triggered by Kinesis Data Streams processes records in batches with invocation and polling latency, and concurrent DynamoDB lookups per shard can throttle. Lambda suits event-driven processing and lightweight transformation, but the question asks which service performs the enrichment itself with minimal latency.
- ✗
AWS Glue streaming ETL
Why it's wrong here
AWS Glue streaming ETL micro-batches from Kinesis with job scheduling overhead, adding latency unsuited to real-time enrichment. It is the right choice for serverless ETL into the Glue Data Catalog or S3 with transformation logic, not for per-record DynamoDB lookups on a live stream.
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.