DEA-C01 Data Ingestion and Transformation Practice Question
A data engineer needs to run a Python-based transformation on each object as it lands in an Amazon S3 bucket. The objects are small (under 10 MB), arrive sporadically, and must be processed within seconds. Which approach is MOST appropriate?
⚠ Common exam trap
The trap here is reaching for a managed ETL service like Glue when the requirement is event-driven per-object processing with seconds-level latency.
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
✓
Configure an S3 event notification to invoke an AWS Lambda function that performs the transformation.
S3 event notifications triggering Lambda is the standard serverless pattern for reacting to individual object uploads in near real time. It avoids polling, scales automatically, and suits small files and sporadic arrivals. Glue, Firehose, and EMR introduce batching, buffering, or cluster overhead that conflicts with the seconds-level, per-object requirement.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Schedule an AWS Glue crawler and ETL job to run every 15 minutes over the bucket.
Why it's wrong here
Glue jobs are designed for batch processing and have startup latency of minutes, so they cannot meet a seconds-level requirement. Running every 15 minutes also batches many objects, losing per-object immediacy. For small, sporadic files needing fast processing, the overhead and schedule granularity make Glue the wrong tool.
- ✗
Run an Amazon EMR cluster with a step function that polls the bucket every minute.
Why it's wrong here
An EMR cluster is heavy infrastructure for small, sporadic files and has significant startup time. Polling every minute adds latency and cost while the cluster idles between arrivals. This over-provisions for a workload that Lambda handles more simply and cheaply with event-driven invocation.
- ✗
Use Amazon Kinesis Data Firehose to deliver objects to a processing endpoint.
Why it's wrong here
Firehose ingests streaming records, not S3 object-creation events. It buffers data based on size or time, which introduces latency and is not triggered per object. Using Firehose here would require an intermediary to read S3 and push records, adding complexity without meeting the per-object requirement.
- ✓
Configure an S3 event notification to invoke an AWS Lambda function that performs the transformation.
Why this is correct
S3 event notifications can trigger Lambda directly on object creation, giving near-real-time, per-object processing without managing servers. Lambda handles small payloads well within its 15-minute limit, and you pay only per invocation, which matches sporadic arrivals and sub-10 MB objects perfectly.
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
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Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official Amazon Web Services exam blueprint
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