MLA-C01 Deployment and Orchestration of ML Workflows Practice Question
A machine learning engineer is using SageMaker Pipelines to orchestrate a training workflow. The pipeline includes a processing step that outputs a dataset, which is then used by a training step. The engineer notices that the processing step runs every time the pipeline executes, even when the input data has not changed. The engineer wants to avoid re-running the processing step if the input data and code are unchanged, while ensuring that downstream steps still execute if the processing step is skipped. Which approach should the engineer take?
⚠ Common exam trap
The trap here is assuming that condition steps can automatically skip steps and reuse outputs, when in fact caching is the native mechanism for avoiding redundant step execution.
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
✓
Enable caching on the processing step by setting the cache policy to CacheConfig with a time-to-live (TTL) and a cache key that includes the input data S3 URI and the processing script's S3 URI.
SageMaker Pipelines supports step caching, where the cache key is derived from the step's input artifacts and parameters. By configuring a cache policy with a TTL on the processing step and ensuring the cache key includes the input data URI and script URI, the step is skipped when these inputs are unchanged. The cached outputs are then used by downstream steps, maintaining pipeline integrity.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Use a condition step to check if the input data has changed, and if not, skip the processing step and directly pass the previous output to the training step.
Why it's wrong here
Condition steps evaluate a condition and branch accordingly, but they cannot automatically retrieve previous outputs or skip steps based on data changes. Implementing such logic would require custom code and would not integrate with SageMaker Pipelines' native caching mechanism, making it error-prone and not the intended solution.
- ✗
Configure the processing step to use a spot instance, which will reduce cost but not affect whether the step runs.
Why it's wrong here
Using spot instances reduces compute cost but does not prevent the step from executing when inputs are unchanged. The requirement is to avoid re-running the step, not to reduce its cost. Spot instances also come with interruption risks, which could complicate pipeline execution.
- ✗
Set the pipeline's execution mode to 'Reprocess' and manually skip the processing step by editing the pipeline definition before each run.
Why it's wrong here
SageMaker Pipelines does not have an execution mode called 'Reprocess'. Manually editing the pipeline definition is not a scalable or automated solution and does not leverage native caching. This approach would require constant intervention and is not recommended for production workflows.
- ✓
Enable caching on the processing step by setting the cache policy to CacheConfig with a time-to-live (TTL) and a cache key that includes the input data S3 URI and the processing script's S3 URI.
Why this is correct
SageMaker Pipelines caching allows a step to be skipped if the cache key (which includes input artifacts and parameters) matches a previous successful run. By including the input data S3 URI and the processing script's S3 URI in the cache key, the step will be skipped when neither has changed. Downstream steps will still run because the processing step's outputs are retrieved from cache.
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Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official Amazon Web Services exam blueprint
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.