DEA-C01 Data Ingestion and Transformation Practice Question
A data engineer is designing a data ingestion pipeline for real-time clickstream data from a website. The data must be stored in Amazon S3 in near-real time, and also be available for real-time analytics using Amazon Athena. The pipeline must handle occasional spikes of up to 10x the normal throughput. Which combination of services should the engineer use?
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
✓
Amazon Kinesis Data Streams with AWS Lambda to write to Amazon S3, and Amazon Athena for queries.
Amazon Kinesis Data Streams can handle high throughput spikes (10x normal) with its scalable stream capacity. AWS Lambda can process records in near-real time and write them to Amazon S3. Amazon Athena can query the data directly from S3 using its schema-on-read approach. Option A is wrong because Amazon SQS is a pull-based queue; Lambda would need to poll SQS, adding latency and complexity for near-real-time ingestion. Option B is wrong because AWS DMS is designed for database migration and continuous replication, not for ingesting real-time clickstream data from a website. Option D is wrong because Amazon Kinesis Data Firehose is a simpler option for streaming data to S3, but the question requires near-real-time availability for Athena; Firehose has a minimum buffer interval of 60 seconds, which may not meet near-real-time requirements, and AWS Glue is typically used for batch ETL rather than low-latency streaming 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.
- ✗
Amazon Simple Queue Service (SQS) with AWS Lambda to write to Amazon S3, and Amazon Athena for queries.
Why it's wrong here
SQS requires polling, adding latency and complexity.
- ✗
AWS Database Migration Service (DMS) to stream data to Amazon S3, and Amazon Athena for queries.
Why it's wrong here
DMS is for database migration, not clickstream.
- ✓
Amazon Kinesis Data Streams with AWS Lambda to write to Amazon S3, and Amazon Athena for queries.
Why this is correct
Kinesis handles spikes, Lambda writes to S3, Athena queries.
- ✗
Amazon Kinesis Data Firehose with AWS Glue for transformation, and Amazon Athena for queries.
Why it's wrong here
Kinesis Data Firehose delivers data to S3 in batches, introducing latency unsuitable for the required real-time analytics with Athena, as data is not immediately queryable upon ingestion. AWS Glue for transformation would further increase this delay. However, Firehose is an excellent choice for cost-effective, managed ingestion of streaming data into a data lake, automatically handling throughput spikes, where near-real-time data *delivery* to S3 is the primary goal and analytics can tolerate some minutes of 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 |
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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.