The answer is Amazon Kinesis Data Streams with Kinesis Data Analytics, then Kinesis Data Firehose to S3. This architecture is correct because Kinesis Data Streams handles high-throughput ingestion of up to 10,000 records per second per shard, while Kinesis Data Analytics performs the compute-intensive sentiment analysis in real-time using Apache Flink or SQL, processing data as it arrives without batching. On the AWS Certified Data Engineer Associate DEA-C01 exam, this scenario tests your understanding of the separation between ingestion, stream processing, and delivery—a common trap is choosing Kinesis Data Firehose alone, which cannot perform compute-intensive transformations, or using Lambda, which has a 15-minute timeout and struggles with sustained 10,000 TPS throughput. The key insight is that Kinesis Data Analytics is purpose-built for low-latency, stateful stream processing, while Firehose handles the buffered delivery to S3. Memory tip: think "Streams for speed, Analytics for compute, Firehose for storage"—the three-step pipeline for real-time, heavy-lifting data flows.
DEA-C01 Data Ingestion and Transformation Practice Question
This DEA-C01 practice question tests your understanding of data ingestion and transformation. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
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?
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.
Option C is 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.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
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.
Related concept
Read the scenario before looking for a memorised answer.
✗
Amazon API Gateway with AWS Lambda to process each tweet and store in S3.
Why it's wrong here
May hit Lambda concurrency limits.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often 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.
Detailed technical explanation
How to think about this question
Kinesis Data Streams uses shards as the base throughput unit, each supporting 1 MB/s or 1,000 records/s for writes; to handle 10,000 TPS, you would provision at least 10 shards (or use on-demand capacity). Kinesis Data Analytics runs Apache Flink internally, which can perform stateful computations like sentiment analysis with exactly-once semantics, and Firehose automatically compresses and partitions data in S3 by time or custom prefixes, reducing downstream ETL costs.
KKey Concepts to Remember
Read the scenario before looking for a memorised answer.
Find the constraint that changes the correct option.
Eliminate answers that are true in general but not in this case.
TExam Day Tips
→Watch for words such as best, first, most likely and least administrative effort.
→Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
A media company stores terabytes of video archives that are accessed once a year for audit purposes. Moving these objects to a cold storage tier (Azure Archive, S3 Glacier, or Google Nearline) costs a fraction of hot storage. Questions like this test whether you understand storage tiers, access frequency tradeoffs, and retrieval latency requirements.
What to study next
Got this wrong? Here's your next step.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Data Ingestion and Transformation — This question tests Data Ingestion and Transformation — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Amazon Kinesis Data Streams with Amazon Kinesis Data Analytics for sentiment analysis, then Kinesis Data Firehose to S3. — Option C is 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.
What should I do if I get this DEA-C01 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Question Discussion
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