Kinesis & Streaming Ingestion — MLA-C01 Practice Question
A representative AWS Machine Learning Engineer Associate (MLA-C01) exam question on Kinesis & Streaming Ingestion. Work through it below, then read why each option is right or wrong.
Short answer
The correct answer is B. Amazon Kinesis Data Firehose delivering directly to an S3 bucket with a buffering hint.
Kinesis Data Firehose is a fully managed delivery service purpose-built to buffer and load streaming data into S3 with no consumer code, custom infrastructure, or scaling to manage — exactly matching "no custom logic, minimize ops."
The Question
A logistics company ingests GPS pings from 50,000 delivery trucks. They need the raw pings durably stored in S3 within a few minutes for later batch feature engineering, but they do not need to run any custom per-record processing logic and want to minimize the operations they manage. Which ingestion approach meets these requirements with the LEAST operational overhead?
Why B is correct
Kinesis Data Firehose is a fully managed delivery service purpose-built to buffer and load streaming data into S3 with no consumer code, custom infrastructure, or scaling to manage — exactly matching "no custom logic, minimize ops."
Why the other options are wrong
Using Kinesis Data Streams plus a custom Lambda consumer requires the team to write and maintain buffering/retry logic that Firehose already provides out of the box, adding unnecessary operational burden.
A self-managed Kafka cluster on EC2 requires patching, scaling, and monitoring the brokers — the highest operational overhead of the options.
MSK removes broker management but still requires building and operating a custom consumer application, which is more overhead than a no-code Firehose delivery stream.
Key idea: Kinesis & Streaming Ingestion
Why B is correct: Kinesis Data Firehose is a fully managed delivery service purpose-built to buffer and load streaming data into S3 with no consumer code, custom infrastructure, or scaling to manage — exactly matching "no custom logic, minimize ops." Why A is wrong: Using Kinesis Data Streams plus a custom Lambda consumer requires the team to write and maintain buffering/retry logic that Firehose already provides out of the box, adding unnecessary operational burden. Why C is wrong: A self-managed Kafka cluster on EC2 requires patching, scaling, and monitoring the brokers — the highest operational overhead of the options. Why D is wrong: MSK removes broker management but still requires building and operating a custom consumer application, which is more overhead than a no-code Firehose delivery stream. On the MLA-C01 exam, questions in the "Data Preparation for Machine Learning" domain test whether you can map a scenario's constraints to the right choice. Read the requirement carefully, eliminate options that violate any single constraint, and pick the one that satisfies all of them with the least operational overhead.
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