DEA-C01 Data Ingestion and Transformation Practice Question
A company is ingesting streaming data from IoT devices into Amazon Kinesis Data Streams. The data must be transformed in real-time using custom Python code before being stored in Amazon S3. Which AWS service should be used to perform this transformation?
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 Analytics for Apache Flink
Amazon EMR with Spark Streaming, which is optimized for large-scale batch and stream processing but is not the simplest or most direct service for this specific use case. Option B is AWS Lambda, which can be used for simple transformations but has limitations on execution time and complexity. Option D is Kinesis Data Firehose with custom data transformation, which supports only built-in transformations or Lambda functions, not arbitrary custom Python code directly. Option C, Kinesis Data Analytics for Apache Flink, is correct because it allows running custom Apache Flink applications, which support custom Python code via the Apache Flink Python API, for real-time data transformation.
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 EMR with Spark Streaming
Why it's wrong here
Amazon EMR with Spark Streaming is optimized for large-scale batch and stream processing but is not the simplest or most direct service for this specific use case. Spark Streaming typically uses Scala or Java, and while PySpark can be used, it adds complexity compared to using Kinesis Data Analytics for Apache Flink with the Python API.
- ✗
AWS Lambda function triggered by Kinesis Data Streams
Why it's wrong here
AWS Lambda lacks the built-in stateful processing and checkpointing required to manage continuous, high-throughput stream transformations without external orchestration. While this approach works for simple, event-driven data enrichment or triggering downstream microservices from discrete Kinesis shards, it fails to provide the native windowing capabilities needed for complex real-time transformations. Amazon Managed Service for Apache Flink provides the necessary stateful processing engine required by this specific architectural requirement.
- ✓
Kinesis Data Analytics for Apache Flink
Why this is correct
Kinesis Data Analytics for Apache Flink is the correct choice because it allows running custom Apache Flink applications that support custom Python code via the Apache Flink Python API, enabling real-time data transformation.
- ✗
Kinesis Data Firehose with custom data transformation
Why it's wrong here
Kinesis Data Firehose can perform data transformation using Lambda functions, but it does not support arbitrary custom Python code directly. It is primarily designed for loading streaming data into destinations like S3 with built-in or Lambda-based transformations.
Quick reference
AWS S3 Storage Class Comparison
| Storage Class | Min Duration | Retrieval | Use Case |
|---|---|---|---|
| S3 Standard | None | Immediate | Frequently accessed data |
| S3 Standard-IA | 30 days | Immediate | Infrequent access, rapid retrieval |
| S3 One Zone-IA | 30 days | Immediate | Non-critical infrequent data |
| S3 Intelligent-Tiering | None | Immediate–hours | Unknown or changing access patterns |
| S3 Glacier Instant | 90 days | Milliseconds | Archive with instant retrieval |
| S3 Glacier Flexible | 90 days | Minutes–hours | Archive, flexible retrieval |
| S3 Glacier Deep Archive | 180 days | Hours | Long-term compliance archive |
Go deeper
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Variation 1. A company ingests streaming data from IoT devices into Amazon Kinesis Data Streams. The data must be transformed in real-time using custom Python code before being stored in Amazon S3. Which AWS service should be used to perform this transformation?
medium- A.Amazon EMR
- B.Amazon Kinesis Data Firehose
- C.AWS Glue
- ✓ D.Amazon Kinesis Data Analytics for Apache Flink
Why D: Amazon Kinesis Data Analytics for Apache Flink enables real-time stream processing with custom Python code via Apache Flink's Python API, making it suitable for complex transformations. Option A (Amazon EMR) is wrong as it requires significant setup and is not a fully managed streaming service. Option B (Amazon Kinesis Data Firehose) is wrong because although it can invoke Lambda for simple transformations, it is limited in complexity and not designed for rich Python custom logic. Option C (AWS Glue) is wrong because it is primarily a batch ETL service and lacks native real-time stream processing capabilities.
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.