MLA-C01 Practice Question: Setting up a data pipeline to ingest streaming…

A company is setting up a data pipeline to ingest streaming clickstream data from their website for real-time analytics and machine learning. The data must be reliably ingested, transformed, and stored in Amazon S3 for batch processing. Which combination of AWS services should be used?

⚠ Common exam trap

A common mix-up: candidates confuse Kinesis Data Streams (a raw streaming layer requiring custom consumers) with Kinesis Data Firehose (a managed delivery service to destinations like S3), often picking Data Streams because it is more commonly discussed, but it does not directly write to S3 without additional components.

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 to Amazon S3

Amazon Kinesis Data Firehose is the correct choice because it is a fully managed service designed to reliably ingest streaming data, transform it (e.g., convert to Parquet/ORC, compress, or invoke AWS Lambda for custom transformations), and automatically deliver it to Amazon S3 without requiring custom code or manual scaling. This directly meets the requirement for real-time ingestion, transformation, and storage in S3 for batch processing.

Answer analysis

Option-by-option breakdown

For each option: why learners choose it and why it is or isn't the right answer here.

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Amazon Kinesis Data Analytics to Amazon S3
Why it's wrong here
Kinesis Data Analytics runs SQL or Flink over streams in flight; it does not itself ingest clickstream data or land it in S3. It is tempting because it is a genuine real-time transformation service, and it would be correct for continuous analytics on an already-ingested stream.
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AWS Glue ETL job to Amazon S3
Why it's wrong here
A Glue ETL job is batch or micro-batch oriented; it does not provide the continuous, reliable streaming ingestion the clickstream source requires. It is tempting because Glue genuinely transforms and writes to S3, and it would be correct for scheduled batch ETL over data already landed in S3.
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Amazon Kinesis Data Firehose to Amazon S3
Why this is correct
Kinesis Data Firehose handles the ingestion, optional transformation, and reliable delivery of streaming clickstream data directly into Amazon S3, satisfying the requirement for near-real-time capture with durable batch storage. It needs no custom consumer code, unlike Kinesis Data Streams, which requires separate processing to land data in S3.
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Amazon Kinesis Data Streams to Amazon SageMaker
Why it's wrong here
SageMaker trains and hosts models; it is not a durable store for transformed clickstream data, so the S3 batch-processing requirement goes unmet. It is tempting because SageMaker is the natural ML destination, and it would be correct when the pipeline's endpoint is model training rather than S3 storage.

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

MLA-C01 Practice Question: Setting up a data pipeline to ingest streaming…

Why each option matters

Option-by-option breakdown

AWS S3 Storage Class Comparison

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