DEA-C01 Data Ingestion and Transformation Practice Question
A company runs an e-commerce platform that generates clickstream data from user interactions on their website. The data is sent as JSON objects via HTTP POST to an API Gateway endpoint, which triggers a Lambda function that writes each record to a Kinesis Data Stream (100 shards). A second Lambda function consumes the stream, transforms the data (enriches with geolocation from a DynamoDB table), and writes to a Kinesis Data Firehose delivery stream that delivers Parquet files to an S3 data lake every 5 minutes. The system has been working for months, but recently the Firehose delivery stream started showing 'DeliveryFailed' errors for a subset of records. The errors point to 'InvalidData' from the Lambda transformation. The engineer reviews the Lambda transformation code and notices that the geolocation lookup occasionally fails because the DynamoDB table has a throttling issue. The engineer needs to handle these failures gracefully so that records that fail enrichment are still delivered to S3 with a null geolocation field, without blocking other records. Which course of action should the engineer take?
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
✓
Modify the Lambda function to catch exceptions during the geolocation lookup, set the geolocation field to null, and continue processing the record.
It modifies the Lambda function to catch exceptions during the geolocation lookup, set the geolocation field to null, and continue processing. This ensures that records that fail enrichment are still delivered to S3 with a null geolocation field, without blocking other records, and without requiring additional infrastructure. Option A is incorrect because Kinesis Data Firehose does not natively support a dead-letter queue (DLQ); failed records can be sent to an S3 bucket for failed data, but that would not include the transformed data with null geolocation. Option B is incorrect because sending failed records to a separate Kinesis Data Stream adds complexity and does not ensure they are delivered to S3 with the desired null geolocation field. Option D is incorrect because increasing RCUs may reduce throttling but does not eliminate the possibility of failures, and it increases cost; the requirement is to handle failures gracefully, not prevent them entirely.
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 the Kinesis Data Firehose delivery stream to send failed records to a dead-letter queue (DLQ) for later reprocessing.
Why it's wrong here
Firehose does not support DLQ natively; it can send failed records to an S3 bucket for failed data, but the records would not be enriched.
- ✗
Modify the Lambda function to send failed records to a separate Kinesis Data Stream for manual processing.
Why it's wrong here
This adds complexity and does not solve the immediate need to deliver records with null geolocation.
- ✓
Modify the Lambda function to catch exceptions during the geolocation lookup, set the geolocation field to null, and continue processing the record.
Why this is correct
This ensures all records are delivered with a default value, maintaining pipeline throughput.
- ✗
Increase the read capacity units (RCUs) on the DynamoDB table to eliminate throttling.
Why it's wrong here
Eliminating throttling may not be possible and increases cost; failures can still occur due to other reasons.
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.