MLA-C01 Deployment and Orchestration of ML Workflows Practice Question
An ML engineer is designing a SageMaker Pipeline for model training and registration. They need to ensure that the pipeline can be re-run with different datasets without manual intervention, and that the steps are only re-executed if inputs have changed. Which THREE features should they configure? (Select THREE.)
⚠ Common exam trap
MLA-C01 often tests the distinction between features that enable reusability and those that provide auditing or conditional logic. Candidates might incorrectly select lineage tracking or condition steps, but the key is to focus on automation and parameterization for re-runs with different datasets.
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 step caching to reuse outputs when inputs are unchanged
Option B is correct because SageMaker Pipelines step caching stores each step's output keyed by a hash of the step's inputs (code, arguments, and input artifacts), so when a pipeline re-runs and a step's inputs are unchanged, the cached output is reused instead of re-executing the step. Option D is correct because parameterized execution lets the pipeline be invoked with different parameter values at runtime (for example via StartPipelineExecution with PipelineParameters), enabling re-runs on new datasets without editing or manually rebuilding the pipeline definition. Option E is correct because defining pipeline parameters for dataset location and hyperparameters is the mechanism that makes the pipeline dynamic and reusable, so the same pipeline definition can be pointed at different data and training configurations. Option A is not appropriate because a Condition step performs branching logic on property values during execution; it does not detect data changes or prevent re-execution, and it would require manual logic rather than automatic cache-based skipping. Option C is not appropriate because lineage tracking records the provenance of artifacts (data, models, jobs) for audit and traceability; it does not control whether steps re-execute or enable parameterized re-runs.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Add a Condition step to manually check for data changes
Why it's wrong here
A Condition step evaluates a boolean property and branches; it cannot detect changed inputs or trigger re-execution automatically. It is tempting because conditional branching suits gating on metrics or approval thresholds, where the pipeline must choose a path rather than cache steps.
- ✓
Enable step caching to reuse outputs when inputs are unchanged
Why this is correct
Step caching stores each step's outputs keyed by its input signature; when a re-run supplies unchanged inputs, SageMaker skips execution and reuses the cached output. This directly satisfies the requirement that steps are only re-executed if inputs have changed.
- ✗
Configure lineage tracking to record the origin of models
Why it's wrong here
Lineage tracking records provenance for audit and reproducibility; it does not parameterise pipeline runs or skip unchanged steps. It is tempting because lineage supports governance and impact analysis, and would be correct where traceability of artefacts and data origins is the requirement.
- ✓
Use Parameterized execution to pass different values at runtime
Why this is correct
Parameterized execution lets the pipeline accept runtime values, such as dataset S3 URIs, without editing the pipeline definition, satisfying the re-run-with-different-datasets requirement. Combined with step caching, unchanged inputs skip re-execution, while altered parameters trigger only affected steps.
- ✓
Define pipeline parameters for dataset location and hyperparameters
Why this is correct
Parameterising dataset location and hyperparameters lets each pipeline run accept new inputs at execution time, satisfying the re-run-with-different-datasets requirement without editing the pipeline definition. SageMaker resolves these parameters when the execution starts, so no manual intervention is needed between runs.
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Last reviewed September 2026 · checked against the official Amazon Web Services exam blueprint
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