DEA-C01 Data Operations and Support Practice Question
A data engineer is designing a data pipeline that ingests millions of small JSON files (1-10 KB each) from an S3 bucket into Amazon Redshift. The current approach uses a Lambda function triggered by S3 events to call the Redshift COPY command for each file. This is causing high latency and throttling. Which alternative is MOST cost-effective and efficient?
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 Amazon Kinesis Data Firehose to buffer and write larger files to S3, then use a scheduled COPY command
Amazon Kinesis Data Firehose can buffer the incoming small JSON files from S3 (via S3 event notifications) and write larger aggregated files to S3. A scheduled COPY command then efficiently loads these larger files into Amazon Redshift, reducing the number of COPY operations and avoiding Lambda throttling. This approach is cost-effective as Firehose charges only for data volume processed, and it eliminates the need for custom batching logic. Other options either process files individually (A, C) or incur higher costs with AWS Glue (D).
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Use Amazon Kinesis Data Streams and a consumer to batch files before COPY
Why it's wrong here
Kinesis Data Streams is for real-time, not optimal for small file aggregation.
- ✓
Use Amazon Kinesis Data Firehose to buffer and write larger files to S3, then use a scheduled COPY command
Why this is correct
Firehose buffers small files into larger ones, reducing COPY frequency and cost.
- ✗
Increase the Lambda concurrency limit and memory
Why it's wrong here
This increases cost and may still throttle.
- ✗
Use AWS Glue to merge files into larger Parquet files before loading
Why it's wrong here
Glue adds overhead and cost; merging can be done more simply.
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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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.