DEA-C01 Data Ingestion and Transformation Practice Question
A data engineer is designing a data ingestion pipeline for IoT sensor data. The data arrives as JSON via AWS IoT Core, and must be stored in Amazon S3 in partitioned Parquet format. The pipeline must handle late-arriving data (up to 1 hour) and ensure exactly-once processing. Which combination of services should the engineer use?
⚠ Common exam trap
Watch out — candidates often choose Kinesis Data Streams with Lambda (Option A) because they think it offers more control, but they overlook that Firehose provides a managed, exactly-once, partitioned Parquet delivery pipeline with built-in late-arriving data handling, which is the exact requirement in the question.
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 Firehose with data transformation via AWS Lambda, delivering to Amazon S3.
Amazon Kinesis Data Firehose is the correct choice because it can directly ingest streaming data from AWS IoT Core, use a built-in AWS Lambda function to transform JSON to Parquet, and deliver the data to Amazon S3 with automatic partitioning. It also supports buffering and retry logic to handle late-arriving data (up to 1 hour) and provides exactly-once delivery to S3 when configured with the appropriate error handling and idempotent transformations.
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 with AWS Lambda for transformation and Amazon S3.
Why it's wrong here
Requires custom exactly-once logic; Lambda has no native Parquet conversion.
- ✗
Amazon Simple Queue Service (SQS) with AWS Lambda for transformation and Amazon S3.
Why it's wrong here
SQS does not natively convert to Parquet or partition data.
- ✗
AWS Glue streaming jobs consuming from Amazon Kinesis Data Streams and writing to Amazon S3.
Why it's wrong here
Glue streaming jobs require deploying and managing a persistent Spark streaming job, including custom checkpointing logic to achieve exactly-once semantics. Kinesis Data Firehose is a fully managed delivery stream with built-in Parquet conversion, partitioning, and buffering — no cluster to provision or manage.
- ✓
Amazon Kinesis Data Firehose with data transformation via AWS Lambda, delivering to Amazon S3.
Why this is correct
Firehose supports Parquet conversion and partitioning; Lambda handles transformation.
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
Related to this question
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Same concept, more angles
2 more ways this is tested on DEA-C01
These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.
Variation 1. A data engineer is designing a data ingestion pipeline for IoT sensor data. The sensors send JSON messages every second. The data must be available in Amazon S3 within 5 minutes and must be transformed (JSON to Parquet) before storage. Which combination of services meets these requirements?
hard- A.Amazon Kinesis Data Streams with AWS Glue streaming ETL
- ✓ B.Amazon Kinesis Data Firehose with data transformation and Parquet conversion
- C.Amazon Kinesis Data Analytics with output to S3
- D.Amazon S3 with S3 Event Notifications to AWS Lambda for transformation
Why B: Amazon Kinesis Data Firehose can ingest streaming data, apply a transformation (e.g., convert JSON to Parquet), and deliver the transformed data to Amazon S3 with a buffer interval of up to 60 seconds, easily meeting the 5-minute latency requirement. Option A is incorrect because AWS Glue streaming ETL adds complexity and is not necessary for simple JSON-to-Parquet conversion; Kinesis Data Firehose handles this natively. Option C is incorrect because Kinesis Data Analytics is designed for real-time analytics and does not directly output to S3 in a transformed format without additional components. Option D is incorrect because S3 Event Notifications to Lambda would incur impractically high invocation costs and latency for per-second sensor data, and transforming on write to S3 would exceed the 5-minute window.
Variation 2. A data engineer is designing a data ingestion pipeline for streaming data from IoT devices. The devices send JSON messages every second. The engineer needs to ingest the data with low latency and store it in Amazon S3 in Parquet format. Which TWO services should the engineer use together?
medium- A.AWS Lambda
- B.Amazon Athena
- ✓ C.Amazon Kinesis Data Streams
- D.AWS Glue
- ✓ E.Amazon Kinesis Data Firehose
Why C: The correct answers are C and E. Amazon Kinesis Data Streams (KDS) provides low-latency, real-time ingestion of streaming data from IoT devices, handling JSON messages sent every second. Amazon Kinesis Data Firehose (KDF) can then consume data from KDS, automatically convert it to Parquet format, and deliver it to Amazon S3. This combination meets the low-latency and Parquet conversion requirements without custom code. Option A (Lambda) could transform data but requires additional management and is not necessary for Parquet conversion as Firehose handles it natively. Option B (Athena) is a query service, not for ingestion. Option D (Glue) is batch-oriented and not suited for low-latency streaming.
JA
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.