DEA-C01 Data Ingestion and Transformation Practice Question
A data engineer is designing a data ingestion pipeline for a social media analytics platform. The pipeline must ingest tweets in real-time, perform sentiment analysis, and store results in Amazon S3. The sentiment analysis is compute-intensive and must be done as the data arrives. The estimated throughput is 10,000 tweets per second. Which architecture is most suitable?
⚠ Common exam trap
Many candidates choose SQS+Lambda (Option A) for simplicity, underestimating the throughput ceiling and polling overhead, while overlooking Kinesis Data Analytics as the only AWS-managed service that natively supports real-time, compute-intensive stream processing without custom infrastructure.
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 Streams with Amazon Kinesis Data Analytics for sentiment analysis, then Kinesis Data Firehose to S3.
The most suitable because Amazon Kinesis Data Streams can ingest up to 10,000 records per second per shard (with shard-level scaling), and Kinesis Data Analytics provides built-in, low-latency stream processing for compute-intensive sentiment analysis using SQL or Apache Flink. Kinesis Data Firehose then reliably buffers and writes the processed results to Amazon S3 without custom code, ensuring near-real-time delivery.
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 SQS with AWS Lambda pollers to process tweets and store in S3.
Why it's wrong here
Not designed for high-throughput streaming.
- ✗
Amazon EMR with Spark Streaming to process tweets and write to S3.
Why it's wrong here
Higher latency and management overhead.
- ✓
Amazon Kinesis Data Streams with Amazon Kinesis Data Analytics for sentiment analysis, then Kinesis Data Firehose to S3.
Why this is correct
Scalable real-time stream processing.
- ✗
Amazon API Gateway with AWS Lambda to process each tweet and store in S3.
Why it's wrong here
May hit Lambda concurrency limits.
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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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.