DEA-C01 Data Ingestion and Transformation Practice Question
A retail company uses Amazon Kinesis Data Firehose to ingest clickstream data from its website into an Amazon S3 bucket. The data includes fields: user_id, event_type, timestamp, page_url. Recently, the data engineering team noticed that some records have malformed JSON (missing commas, extra brackets) causing delivery failures to S3. The Firehose delivery stream is configured to retry failed records for 300 seconds, after which the records are sent to an S3 bucket for failed records. The team wants to transform the data to correct malformed JSON before delivery to the main S3 bucket. They need a solution that does not require managing servers and can handle high throughput. What should the team do?
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 AWS Lambda function as a data transformation in Kinesis Data Firehose to correct malformed JSON.
AWS Lambda can be used as a data transformation function within Kinesis Data Firehose to process each record individually, fixing malformed JSON without managing servers. The Lambda function receives records in batches, transforms them (e.g., parsing and correcting JSON syntax), and returns them to Firehose for delivery to S3. This serverless approach scales automatically with high throughput. Option B (EMR with Spark) requires managing a cluster and adds latency, making it less suitable for real-time streaming. Option C (Glue streaming ETL) is serverless but introduces more complexity and potential latency compared to Firehose's built-in Lambda transformation. Option D (Kinesis Data Analytics) is designed for real-time analytics and not for record-level transformations like JSON correction.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Configure an AWS Lambda function as a data transformation in Kinesis Data Firehose to correct malformed JSON.
Why this is correct
Firehose supports Lambda transformations for record-level processing; it scales automatically.
- ✗
Set up an Amazon EMR cluster with Apache Spark to process the data in micro-batches and fix JSON errors.
Why it's wrong here
EMR requires cluster management and is overkill for simple JSON correction.
- ✗
Use an AWS Glue streaming ETL job to read from Firehose and write corrected data to S3.
Why it's wrong here
AWS Glue streaming ETL jobs are designed for serverless, high-throughput real-time data processing, making them suitable for complex transformations from Kinesis Data Streams to S3. However, this scenario requires transforming data *within* the Kinesis Data Firehose delivery stream *before* it attempts delivery to the main S3 bucket. Firehose offers a native, in-line data transformation feature, typically using AWS Lambda, to correct malformed records directly, preventing them from being routed to the failed records S3 bucket.
- ✗
Use Amazon Kinesis Data Analytics with a SQL application to parse and fix JSON.
Why it's wrong here
Kinesis Data Analytics is designed for real-time analytics, not for individual record 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
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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.