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MLA-C01 Practice Question: A data engineer needs to ingest streaming…
A data engineer needs to ingest streaming clickstream data from a website into an S3 data lake for ML training, with the ability to run real-time aggregations before storage. Which combination of AWS services meets these requirements?
⚠ Common exam trap
Many exam-takers confuse Kinesis Data Firehose's ability to invoke Lambda for simple transformations with the need for real-time aggregations, overlooking that Kinesis Data Analytics is the only service that provides continuous, stateful stream processing required for real-time aggregations before storage.
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 → Kinesis Data Analytics → Kinesis Data Firehose → S3
It provides a complete pipeline for both real-time aggregation and durable storage. Kinesis Data Streams ingests the streaming clickstream data, Kinesis Data Analytics performs real-time SQL or Apache Flink-based aggregations on the stream, and Kinesis Data Firehose delivers the aggregated results to S3 with optional data transformation and buffering, meeting the requirements for ML training.
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 Kinesis Data Streams → Kinesis Data Analytics → Kinesis Data Firehose → S3
Why this is correct
Kinesis Data Analytics performs the real-time aggregation the stem requires before storage, while Firehose handles reliable micro-batch delivery into S3. Data Streams ingests the clickstream continuously, satisfying both the streaming ingestion and pre-storage aggregation constraints in one pipeline.
- ✗
Amazon Kinesis Data Streams → Amazon SageMaker Data Wrangler → S3
Why it's wrong here
SageMaker Data Wrangler is for interactive data preparation, not real-time streaming ingestion.
- ✗
Amazon Kinesis Data Firehose → AWS Glue ETL → S3
Why it's wrong here
AWS Glue is batch-oriented, not suitable for real-time streaming.
- ✗
Amazon SQS → AWS Lambda → S3
Why it's wrong here
SQS is for message queuing, not streaming; Lambda can process but lacks native streaming ingestion optimizations.
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 MLA-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 MLA-C01 exam.