- A
Use Amazon S3 Transfer Acceleration with S3 Event Notifications to trigger AWS Lambda for processing.
Why wrong: S3 Transfer Acceleration accelerates uploads to S3 but is not designed for streaming ingestion; S3 Event Notifications have latency and are not ideal for near-real-time.
- B
Use Amazon Kinesis Data Firehose to ingest data into Amazon S3 and use AWS Lambda to transform data during delivery.
Why wrong: Kinesis Data Firehose buffers data before delivering to S3, which introduces latency and is not suitable for sub-second analytics.
- C
Use Amazon Simple Queue Service (SQS) to buffer the streaming data and configure an Auto Scaling group of EC2 instances to poll and process the data.
Why wrong: SQS is a message queue, not a streaming platform; it does not support partitioning, replay, or ordered processing across shards.
- D
Use Amazon Kinesis Data Streams to ingest the data and AWS Lambda to process records in real-time with automatic scaling.
Kinesis Data Streams provides durable, scalable, low-latency ingestion; Lambda can process each shard in parallel and scales automatically.
DEA-C01 Data Ingestion and Transformation Practice Question
This DEA-C01 practice question tests your understanding of data ingestion and transformation. Match the stated requirement to the specific cloud service, access model, or configuration option — many options are valid in isolation but not for this scenario. 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 needs to ingest streaming data from thousands of IoT devices into AWS for near-real-time analytics. The data volume varies significantly and can spike unpredictably. The engineer wants to minimize operational overhead and ensure that data is durably stored as soon as it arrives. Which AWS service combination should the engineer use?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue:
"minimum / minimize"Why it matters: Asks for the least resource use — fewest addresses, smallest subnet, lowest overhead. Eliminate over-provisioned options even if they would technically work.
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 Amazon Kinesis Data Streams to ingest the data and AWS Lambda to process records in real-time with automatic scaling.
Amazon Kinesis Data Streams (KDS) is designed for ingesting large volumes of streaming data with automatic scaling (via shard splitting/merging) and provides durable storage (default 24-hour retention, extendable to 365 days) as soon as records are received. AWS Lambda can be subscribed to the stream to process records in near-real-time, scaling automatically based on the number of shards, which minimizes operational overhead and handles unpredictable spikes without manual intervention.
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.
- ✗
Use Amazon S3 Transfer Acceleration with S3 Event Notifications to trigger AWS Lambda for processing.
Why it's wrong here
S3 Transfer Acceleration accelerates uploads to S3 but is not designed for streaming ingestion; S3 Event Notifications have latency and are not ideal for near-real-time.
- ✗
Use Amazon Kinesis Data Firehose to ingest data into Amazon S3 and use AWS Lambda to transform data during delivery.
Why it's wrong here
Kinesis Data Firehose buffers data before delivering to S3, which introduces latency and is not suitable for sub-second analytics.
- ✗
Use Amazon Simple Queue Service (SQS) to buffer the streaming data and configure an Auto Scaling group of EC2 instances to poll and process the data.
Why it's wrong here
SQS is a message queue, not a streaming platform; it does not support partitioning, replay, or ordered processing across shards.
- ✓
Use Amazon Kinesis Data Streams to ingest the data and AWS Lambda to process records in real-time with automatic scaling.
Why this is correct
Kinesis Data Streams provides durable, scalable, low-latency ingestion; Lambda can process each shard in parallel and scales automatically.
Clue confirmation
The clue word "minimum / minimize" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often confuse Kinesis Data Firehose (which batches and delivers to destinations like S3) with Kinesis Data Streams (which provides real-time, durable storage and processing), leading them to choose Option B despite its lack of true near-real-time ingestion and automatic scaling for spikes.
Detailed technical explanation
How to think about this question
Kinesis Data Streams uses shards as the unit of throughput, each supporting 1 MB/s write and 2 MB/s read, and can automatically scale using on-demand mode or manual shard operations. The Lambda integration uses event source mappings that poll each shard in parallel, with a configurable batch window and error handling via a dead-letter queue, ensuring exactly-once processing semantics per record. In a real-world scenario with thousands of IoT devices, KDS can handle bursty traffic by splitting hot shards, while Firehose would buffer data and risk backpressure or data loss if the buffer fills before delivery.
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.
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 |
What to study next
Got this wrong? Here's your next step.
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FAQ
Questions learners often ask
What does this DEA-C01 question test?
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: Use Amazon Kinesis Data Streams to ingest the data and AWS Lambda to process records in real-time with automatic scaling. — Amazon Kinesis Data Streams (KDS) is designed for ingesting large volumes of streaming data with automatic scaling (via shard splitting/merging) and provides durable storage (default 24-hour retention, extendable to 365 days) as soon as records are received. AWS Lambda can be subscribed to the stream to process records in near-real-time, scaling automatically based on the number of shards, which minimizes operational overhead and handles unpredictable spikes without manual intervention.
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.
Are there clue words in this question I should notice?
Yes — watch for: "minimum / minimize". Asks for the least resource use — fewest addresses, smallest subnet, lowest overhead. Eliminate over-provisioned options even if they would technically work.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Last reviewed: Jul 4, 2026
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