The answer is Amazon Kinesis Data Streams to Amazon Kinesis Data Firehose to S3. This combination is the most cost-effective because Kinesis Data Streams provides durable, real-time ingestion for high-throughput sensor data, while Kinesis Data Firehose natively handles buffering, transformation (such as converting JSON to Parquet), and reliable delivery to S3—all without requiring additional compute resources. On the AWS Certified Data Engineer Associate DEA-C01 exam, this scenario tests your understanding of streaming versus batch services and the specific role of Firehose as a managed delivery service with built-in transformation capabilities. A common trap is choosing AWS Lambda for transformation, but that approach incurs per-invocation costs and scaling overhead for thousands of devices, making it less cost-effective than Firehose’s native Parquet conversion. Another pitfall is selecting Glue, which is batch-oriented and unsuitable for sub-minute latency. Remember the mnemonic: “Streams for speed, Firehose for format”—Kinesis handles the real-time ingestion, and Firehose handles the file conversion and landing.
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 company needs to ingest real-time sensor data from thousands of IoT devices into Amazon S3, with a latency of less than 1 minute. The data must be transformed (e.g., convert to Parquet) before landing in S3. Which combination of services is MOST cost-effective?
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 to Amazon Kinesis Data Firehose to S3.
Option A is correct because Kinesis Data Streams ingests data in real-time, and Kinesis Data Firehose can buffer, transform (e.g., convert to Parquet), and deliver to S3 with low latency. Option B is wrong because Kinesis Data Analytics is for running SQL on streams, not for simple transformation. Option C is wrong because Glue is batch-oriented, not real-time streaming. Option D is wrong because Lambda can transform but may not scale cost-effectively for thousands of devices.
Key principle: NAT direction and interface roles matter as much as the IP address mapping. Inside/outside designation controls which traffic is translated.
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 Streams to AWS Lambda to S3.
Why it's wrong here
Lambda can be costly for high-frequency invocations from many devices.
✗
Amazon Kinesis Data Streams to Amazon Kinesis Data Analytics to S3.
Why it's wrong here
KDA is for complex analytics, not simple transformation; adds cost.
✗
Amazon Kinesis Data Streams to AWS Glue streaming ETL to S3.
Why it's wrong here
Glue streaming ETL is more expensive and designed for batch-oriented streaming.
✓
Amazon Kinesis Data Streams to Amazon Kinesis Data Firehose to S3.
Why this is correct
Firehose can transform and deliver with low latency, cost-effective for high throughput.
Related concept
Static NAT maps one inside address to one outside address.
Common exam traps
Common exam trap: NAT rules depend on direction and matching traffic
NAT is not only about the public address. The inside/outside interface roles and the ACL or rule that matches traffic are just as important.
Detailed technical explanation
How to think about this question
NAT questions usually test address translation, overload/PAT behaviour, static mappings and whether the right traffic is being translated. Read the interface direction and address terms carefully.
KKey Concepts to Remember
Static NAT maps one inside address to one outside address.
PAT allows many inside hosts to share one public address using ports.
Inside local and inside global describe the private and translated addresses.
NAT ACLs identify traffic for translation, not always security filtering.
TExam Day Tips
→Identify inside and outside interfaces first.
→Check whether the scenario needs static NAT, dynamic NAT or PAT.
→Do not confuse NAT matching ACLs with normal packet-filtering intent.
Key takeaway
NAT direction and interface roles matter as much as the IP address mapping. Inside/outside designation controls which traffic is translated.
Real-world example
How this comes up in practice
A startup's cloud architect reviews their monthly bill and notices costs are higher than expected for a long-running batch job. Switching from on-demand instances to Reserved Instances — or using Spot/Preemptible VMs — can reduce compute costs by up to 72 %. Questions like this test whether you understand the tradeoffs between commitment, flexibility, and cost across cloud pricing models.
What to study next
Got this wrong? Here's your next step.
Review the four NAT address types (inside local, inside global, outside local, outside global), PAT port overload, and static vs dynamic NAT use cases. Then practise related DEA-C01 NAT questions on configuration and troubleshooting.
Data Ingestion and Transformation — This question tests Data Ingestion and Transformation — Static NAT maps one inside address to one outside address..
What is the correct answer to this question?
The correct answer is: Amazon Kinesis Data Streams to Amazon Kinesis Data Firehose to S3. — Option A is correct because Kinesis Data Streams ingests data in real-time, and Kinesis Data Firehose can buffer, transform (e.g., convert to Parquet), and deliver to S3 with low latency. Option B is wrong because Kinesis Data Analytics is for running SQL on streams, not for simple transformation. Option C is wrong because Glue is batch-oriented, not real-time streaming. Option D is wrong because Lambda can transform but may not scale cost-effectively for thousands of devices.
What should I do if I get this DEA-C01 question wrong?
Review the four NAT address types (inside local, inside global, outside local, outside global), PAT port overload, and static vs dynamic NAT use cases. Then practise related DEA-C01 NAT questions on configuration and troubleshooting.
What is the key concept behind this question?
Static NAT maps one inside address to one outside address.
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Question Discussion
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