DEA-C01 Data Ingestion and Transformation Practice Question
A data pipeline uses Amazon Kinesis Data Firehose to ingest log data from web servers and deliver it to Amazon S3. The data is then transformed by an AWS Glue job before being loaded into Amazon Redshift. The pipeline must handle a sudden spike in log volume without data loss. Which configuration change is MOST appropriate?
⚠ Common exam trap
Many exam-takers confuse downstream scaling (Redshift or Glue) with ingestion-layer fault tolerance, overlooking that Kinesis Data Firehose’s S3 backup directly addresses data loss at the point of delivery failure.
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 Kinesis Data Firehose to back up all data to S3 in case of delivery failures.
Kinesis Data Firehose can be configured to back up all data to Amazon S3 in case of delivery failures, ensuring no data loss during spikes. This feature writes incoming data to a separate S3 bucket as a safety net when the primary destination (e.g., Redshift via Glue) is unavailable or overwhelmed, directly addressing the requirement to handle sudden volume spikes without data loss.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Increase the AWS Glue job timeout and allocate more DPUs.
Why it's wrong here
Glue job timeout does not prevent data loss during Firehose ingestion.
- ✓
Configure Kinesis Data Firehose to back up all data to S3 in case of delivery failures.
Why this is correct
S3 backup for failed records ensures no data loss.
- ✗
Increase the number of nodes in the Redshift cluster to handle higher load.
Why it's wrong here
Redshift capacity does not affect Firehose ingestion.
- ✗
Increase the S3 bucket size limit and enable versioning.
Why it's wrong here
Bucket size limits are not a concern; versioning helps with accidental deletion but not with Firehose 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 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.