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MLA-C01 Practice Question: A data scientist needs to ingest streaming…
A data scientist needs to ingest streaming customer clickstream data from a website into an S3 data lake for ML training. The data must be delivered within 1 minute of ingestion, and JSON records must be converted to Parquet. 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 a 60-second buffer and Parquet conversion enabled
Amazon Kinesis Data Firehose can buffer incoming data and deliver to S3 with a 60-second buffer window, and it supports converting JSON to Parquet. KDS alone does not convert to Parquet. Glue ETL can do the conversion but adds latency. Lambda with S3 trigger is not streaming-oriented.
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 S3 Transfer Acceleration with direct PUT requests by clients
Why it's wrong here
S3 Transfer Acceleration speeds up uploads over long distances via edge locations; it neither ingests continuous clickstream events nor converts JSON to Parquet. It is tempting because it accelerates client PUTs, but the stem requires sub-minute streaming delivery plus format conversion.
- ✗
Amazon Kinesis Data Streams (KDS) with a Lambda consumer that writes JSON to S3
Why it's wrong here
Kinesis Data Streams with a Lambda consumer delivers within seconds and writes to S3, but Lambda writes the JSON records unchanged, so the required JSON-to-Parquet conversion is absent. It would be correct if Parquet conversion were unnecessary or handled downstream.
- ✗
AWS Glue ETL job triggered every minute to pull from Kinesis Data Streams and write Parquet to S3
Why it's wrong here
Glue ETL jobs run on a minimum one-minute schedule, so records can wait up to 60 seconds before processing, breaching the sub-minute delivery requirement. Glue is designed for batch and micro-batch transformation, and would be the right choice when latency of a minute or more is acceptable.
- ✓
Amazon Kinesis Data Firehose with a 60-second buffer and Parquet conversion enabled
Why this is correct
Firehose buffers incoming records and delivers them within the configured 60-second window, satisfying the one-minute latency constraint, while its built-in record format conversion transforms JSON into Parquet using a Glue table schema before writing to the S3 data lake.
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.