- A
AWS Glue
AWS Glue can be used for streaming ETL transformations, such as adding timestamps and filtering malformed records, making it a valid component of the pipeline.
- B
Amazon Athena
Why wrong: Amazon Athena is an interactive query service for analyzing data in S3, not for ingestion or transformation in a streaming pipeline.
- C
Amazon Simple Queue Service (SQS)
Why wrong: Amazon SQS is a message queuing service; while it can decouple components, it is not needed here as IoT Core can directly forward to Firehose.
- D
AWS IoT Core
AWS IoT Core is essential for securely ingesting IoT sensor data via MQTT/HTTPS and can route messages to Firehose via rules.
- E
Amazon Kinesis Data Firehose
Amazon Kinesis Data Firehose ingests streaming data from IoT Core, can buffer and optionally transform data, and delivers it to S3 within the required latency.
Ingesting IoT Sensor Data to S3 with AWS IoT Core and Kinesis Firehose
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. A key principle to apply: aWS IoT Core. 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 IoT sensor data. The sensors send JSON messages every second, and the data must be stored in Amazon S3 in near real-time (within 5 minutes). The engineer also needs to transform the data by adding a timestamp and filtering out malformed records. Which THREE services should be used together?
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
AWS Glue
AWS IoT Core (D) securely ingests sensor data via MQTT and routes it to a Kinesis data stream using a rule. AWS Glue Streaming ETL (A) consumes from the stream, adds a timestamp, filters malformed records, and writes the cleaned data to Kinesis Data Firehose (E). Firehose buffers and delivers the data to Amazon S3 within minutes, meeting the near-real-time requirement.
Key principle: AWS IoT Core
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
AWS Glue
Why this is correct
AWS Glue can be used for streaming ETL transformations, such as adding timestamps and filtering malformed records, making it a valid component of the pipeline.
Related concept
AWS IoT Core
- ✗
Amazon Athena
Why it's wrong here
Amazon Athena is an interactive query service for analyzing data in S3, not for ingestion or transformation in a streaming pipeline.
- ✗
Amazon Simple Queue Service (SQS)
Why it's wrong here
Amazon SQS is a message queuing service; while it can decouple components, it is not needed here as IoT Core can directly forward to Firehose.
- ✓
AWS IoT Core
Why this is correct
AWS IoT Core is essential for securely ingesting IoT sensor data via MQTT/HTTPS and can route messages to Firehose via rules.
Related concept
AWS IoT Core
- ✓
Amazon Kinesis Data Firehose
Why this is correct
Amazon Kinesis Data Firehose ingests streaming data from IoT Core, can buffer and optionally transform data, and delivers it to S3 within the required latency.
Related concept
AWS IoT Core
Common exam traps
Common exam trap: answer the scenario, not the keyword
Candidates often mistakenly think that Kinesis Data Firehose can be consumed directly by Glue Streaming ETL, but Glue actually reads from a Kinesis Data Streams. The correct pipeline uses IoT Core to route to a Data Stream, Glue to transform, and Firehose to deliver to S3.
Detailed technical explanation
How to think about this question
AWS IoT Core rules use a SQL-like syntax to filter and transform incoming MQTT messages before routing them to destinations like Kinesis Data Firehose. Kinesis Data Firehose buffers data up to 60 seconds or 1 MB before writing to S3, ensuring near-real-time delivery while allowing a Lambda function for custom transformations (e.g., adding timestamps). In practice, this architecture scales to millions of devices by leveraging IoT Core's device gateway and Firehose's automatic partitioning, avoiding the need for custom ingestion code.
KKey Concepts to Remember
- AWS IoT Core
- AWS Glue Streaming ETL
- Amazon Kinesis Data Firehose
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
AWS IoT Core
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.
Review aWS IoT Core, then practise related DEA-C01 questions on the same topic to reinforce the concept.
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Data Ingestion and Transformation — study guide chapter
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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 — AWS IoT Core.
What is the correct answer to this question?
The correct answer is: AWS Glue — AWS IoT Core (D) securely ingests sensor data via MQTT and routes it to a Kinesis data stream using a rule. AWS Glue Streaming ETL (A) consumes from the stream, adds a timestamp, filters malformed records, and writes the cleaned data to Kinesis Data Firehose (E). Firehose buffers and delivers the data to Amazon S3 within minutes, meeting the near-real-time requirement.
What should I do if I get this DEA-C01 question wrong?
Review aWS IoT Core, then practise related DEA-C01 questions on the same topic to reinforce the concept.
What is the key concept behind this question?
AWS IoT Core
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Last reviewed: Jul 4, 2026
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