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MLA-C01 Practice Question: A machine learning engineer needs to ingest…
A machine learning engineer needs to ingest streaming data from thousands of IoT devices into Amazon S3 for batch training. The data should be available in S3 within minutes of arrival. Which combination of services should the engineer use?
⚠ Common exam trap
Many candidates choose AWS IoT Core (Option B) because it seems IoT-specific, but they overlook that IoT Core does not natively stream data into S3 with low latency—it requires an additional integration like Kinesis or Lambda, making the direct Kinesis Data Streams + Firehose pipeline the correct and simpler choice.
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 and Amazon Kinesis Data Firehose
Amazon Kinesis Data Streams ingests and stores streaming data from thousands of IoT devices durably, while Amazon Kinesis Data Firehose automatically delivers that data to Amazon S3 with near-real-time latency (typically 60–90 seconds). This combination provides the required buffering, scaling, and direct S3 integration without custom code, meeting the 'within minutes' requirement for batch training data.
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 and Amazon Kinesis Data Firehose
Why this is correct
Kinesis Data Streams ingests high-throughput data; Kinesis Data Firehose buffers and delivers data to S3 within minutes.
- ✗
AWS IoT Core and Amazon DynamoDB Streams
Why it's wrong here
IoT Core connects devices, but DynamoDB Streams is for CDC from DynamoDB, not for direct S3 delivery.
- ✗
Amazon SQS and AWS Lambda
Why it's wrong here
SQS and Lambda can process streaming data, but they are not optimized for high-throughput IoT ingestion and may not meet the sub-minute delivery requirement consistently.
- ✗
Amazon Kinesis Data Analytics and AWS Glue ETL
Why it's wrong here
Kinesis Data Analytics is for real-time analytics, not storage. AWS Glue ETL is batch-oriented and not designed for streaming to S3 in minutes.
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 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.