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MLA-C01 Practice Question: A data scientist needs to ingest streaming…
A data scientist needs to ingest streaming clickstream data from a website into an S3 data lake for ML training. The data must be processed in near real-time and partitioned by hour. Which AWS service combination should be used?
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 Firehose with S3 as destination and dynamic partitioning enabled
Amazon Kinesis Data Firehose can directly stream data to S3 with dynamic partitioning, enabling automatic hourly partitioning for near real-time ingestion. Option A is correct. Option B (S3 Transfer Acceleration) is for speeding up large uploads over long distances, not for streaming or partitioning. Option C requires a custom consumer to write to S3, adding complexity without automatic partitioning. Option D uses a batch-oriented ETL job, not designed for near real-time streaming.
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 Firehose with S3 as destination and dynamic partitioning enabled
Why this is correct
Kinesis Data Firehose delivers streaming data to S3 and supports dynamic partitioning, which derives partition keys from incoming record fields. This satisfies the hourly partitioning requirement without custom consumers, while buffering provides the near real-time ingestion the clickstream pipeline needs.
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Amazon S3 Transfer Acceleration with direct uploads from the website
Why it's wrong here
Transfer Acceleration speeds uploads over long distances using edge locations, but it cannot ingest continuous clickstream events or partition output by hour. It suits bulk uploads of large files from distributed clients; near-real-time streaming with hourly partitioning requires a streaming service feeding S3.
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Amazon Kinesis Data Streams with a custom consumer writing to S3
Why it's wrong here
Kinesis Data Streams ingests the clickstream, but a custom consumer must implement hourly partitioning, checkpointing and error handling itself, which the stem's managed near-real-time requirement does not demand. Custom consumers suit bespoke processing logic; Firehose delivers buffered, partitioned output to S3 natively.
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AWS Glue ETL job reading from Kinesis Data Streams and writing to S3
Why it's wrong here
A Glue ETL job reading Kinesis runs on a schedule or trigger, adding batch latency rather than the near-real-time ingestion required. Glue suits transforming and cataloguing data already landed in S3; Kinesis Data Firehose handles continuous delivery with native hourly prefix partitioning.
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.