DEA-C01 Data Ingestion and Transformation Practice Question
A social media company ingests user activity data from multiple sources using Amazon Kinesis Data Firehose. The data is delivered to Amazon S3 in near-real-time. The company wants to transform the data by adding a timestamp and masking email addresses before storing it in S3. The transformation should be applied to all records. What is the most cost-effective way to implement this transformation?
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 the Firehose delivery stream to invoke a Lambda function for data transformation.
The most cost-effective way is to configure the Kinesis Data Firehose delivery stream to invoke an AWS Lambda function for data transformation before the data is delivered to S3. This approach is serverless and only runs when data is flowing, so you pay only for the compute time used during transformation. Option A (Athena CTAS) would require querying after storage, adding cost and latency. Option B (Glue batch job) runs on a schedule regardless of data volume, leading to idle costs, and introduces latency. Option C (S3 Events with Lambda) triggers after data is already stored, meaning the data is first stored in raw form, then transformed, doubling storage costs and adding complexity. Therefore, the Firehose-integrated Lambda is the most cost-effective and low-latency 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 Athena to run a CTAS query that transforms the data and writes to a new location.
Why it's wrong here
Athena CTAS queries operate on data *after* it is already stored in S3, not during ingestion; the requirement demands transformation *before* storage, which Kinesis Data Firehose’s built-in Lambda integration performs in-stream. This option is tempting because Athena is cost-effective for ad-hoc analytical transformations on historical data, and would be correct if the company needed to reprocess already-landed data rather than transform records in near-real-time during delivery.
- ✗
Use AWS Glue to schedule a batch job every 5 minutes to transform the data.
Why it's wrong here
Batch processing adds latency and is not cost-effective for near-real-time.
- ✗
Use Amazon S3 Events to trigger a Lambda function whenever a new object is created.
Why it's wrong here
This would process files after they are stored, not before, and incurs additional S3 costs.
- ✓
Configure the Firehose delivery stream to invoke a Lambda function for data transformation.
Why this is correct
Firehose supports built-in Lambda transformation for real-time processing.
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 |
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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.