Endpoint Selection & Batch Transform — MLA-C01 Practice Question
A representative AWS Machine Learning Engineer Associate (MLA-C01) exam question on Endpoint Selection & Batch Transform. Work through it below, then read why each option is right or wrong.
Short answer
The correct answer is B. SageMaker Batch Transform.
Batch Transform is purpose-built for scoring large offline datasets stored in S3: it spins up the compute, processes the entire dataset, writes output to S3, and shuts down — no endpoint needs to stay up between weekly runs.
The Question
A biotech research lab needs to run genomic variant classification against a 40 TB archive of sequencing files stored in Amazon S3. The job runs once a week, does not need a live API, and should just write predictions back to S3 when complete. Which inference option is MOST appropriate?
Why B is correct
Batch Transform is purpose-built for scoring large offline datasets stored in S3: it spins up the compute, processes the entire dataset, writes output to S3, and shuts down — no endpoint needs to stay up between weekly runs.
Why the other options are wrong
A real-time endpoint (multi-model or not) keeps infrastructure running continuously and is meant for on-demand request/response traffic, not a scheduled offline sweep of 40 TB.
Asynchronous Inference still requires a persistent endpoint and is designed for single large requests routed through a queue, not for iterating over an entire dataset in S3.
Serverless Inference is for lightweight, intermittent request/response calls with a payload limit; it is not designed to process a 40 TB dataset in one job.
Key idea: Endpoint Selection & Batch Transform
Why B is correct: Batch Transform is purpose-built for scoring large offline datasets stored in S3: it spins up the compute, processes the entire dataset, writes output to S3, and shuts down — no endpoint needs to stay up between weekly runs. Why A is wrong: A real-time endpoint (multi-model or not) keeps infrastructure running continuously and is meant for on-demand request/response traffic, not a scheduled offline sweep of 40 TB. Why C is wrong: Asynchronous Inference still requires a persistent endpoint and is designed for single large requests routed through a queue, not for iterating over an entire dataset in S3. Why D is wrong: Serverless Inference is for lightweight, intermittent request/response calls with a payload limit; it is not designed to process a 40 TB dataset in one job. On the MLA-C01 exam, questions in the "Deployment and Orchestration of ML Workflows" 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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