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?
⚠ Common exam trap
DEA-C01 often tests whether candidates choose 'route failures elsewhere' (DLQ, separate stream) versus 'handle failures inline and continue' — the trap is picking DLQ because it sounds robust, when the requirement is to still deliver the record with a null field.
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.
The requirement is to keep records flowing to S3 with a null geolocation when enrichment fails, without blocking other records. Wrapping the DynamoDB lookup in a try/catch inside the Lambda transformation, setting geolocation to null on failure, and returning the record as 'Ok' ensures Firehose treats it as successfully transformed and delivers it. This is the standard graceful-degradation pattern for Firehose Lambda 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.
- ✗
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 DLQ captures records that fail delivery, not records the Lambda transform rejects as InvalidData; the stem requires failed enrichment to still reach S3 with a null geolocation. DLQ is correct when you must preserve undeliverable payloads for later manual reprocessing rather than continue the pipeline.
- ✗
Modify the Lambda function to send failed records to a separate Kinesis Data Stream for manual processing.
Why it's wrong here
Redirecting failed records to another Kinesis stream still requires downstream processing and does not satisfy the requirement that they reach S3 with a null geolocation field. It is tempting because dead-letter queues isolate poison records, which suits discarding or replaying them later, but here enrichment failures must be delivered, not diverted.
- ✓
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
Wrapping the DynamoDB geolocation lookup in try/catch lets the function substitute a null geolocation and emit the record rather than throwing, which is what currently produces the InvalidData delivery failures. This satisfies the requirement that unenriched records still reach S3 without blocking other records in the batch.
- ✗
Increase the read capacity units (RCUs) on the DynamoDB table to eliminate throttling.
Why it's wrong here
Raising RCUs addresses the DynamoDB throttling cause but leaves the Lambda transform without error handling, so any lookup failure still emits InvalidData and Firehose rejects the record. Provisioning capacity is correct when sustained read demand exceeds the table's throughput, not when the code must tolerate transient lookup failures.
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 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
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.