DEA-C01 Data Ingestion and Transformation Practice Question
A team is designing a data ingestion pipeline to load JSON files from an Amazon S3 bucket into Amazon Redshift. The files arrive every 5 minutes, and each file is between 10 MB and 50 MB. The team wants to minimize the time between file arrival and data availability in Redshift. Which approach should the team use?
⚠ Common exam trap
A common mix-up: candidates confuse Redshift Spectrum (which queries external data without loading) with the requirement to have data available in Redshift tables, or they may overestimate the suitability of scheduled Glue jobs for low-latency, frequent ingestion.
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
✓
Use S3 Event Notifications to trigger an AWS Lambda function that runs the COPY command to load data into Redshift.
S3 Event Notifications can trigger an AWS Lambda function that executes the COPY command, loading data into Redshift with minimal latency. This approach avoids the overhead of scheduling or batching, directly responding to each file arrival to meet the 5-minute frequency and file size requirements.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Schedule an AWS Glue job to run every 5 minutes to load the data.
Why it's wrong here
Scheduling an AWS Glue job every 5 minutes introduces a minimum 5-minute latency window even if files arrive earlier, failing the requirement to minimise time between file arrival and data availability. This approach is tempting because AWS Glue is purpose-built for batch ETL transformations on semi-structured data like JSON, and would be correct if the team needed complex schema-on-read transformations or file format conversions before loading.
- ✓
Use S3 Event Notifications to trigger an AWS Lambda function that runs the COPY command to load data into Redshift.
Why this is correct
Lambda responds quickly to S3 events and runs COPY for efficient bulk loading.
- ✗
Use Amazon Redshift Spectrum to query the data directly from S3 without loading.
Why it's wrong here
Spectrum queries external tables but does not load data into Redshift; query performance may be slower.
- ✗
Configure Amazon Kinesis Data Firehose to stream data from S3 to Redshift.
Why it's wrong here
Firehose expects a stream, not batch files; it would require additional transformation and adds latency.
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
Related to this question
About these practice questions
This DEA-C01 question is part of Courseiva's 1,711-question bank — original exam-style content with full explanations and wrong-answer analysis, never real exam questions or exam dumps. Learn why practice questions differ from exam dumps →
JA
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.