easyMultiple SelectObjective-mapped
MLA-C01 Practice Question: Adopting Amazon SageMaker Pipelines to automate…
A company is adopting Amazon SageMaker Pipelines to automate their ML workflow. They want to choose three key benefits that SageMaker Pipelines provides over traditional manual scripts and ad-hoc steps. Which THREE benefits are correct?
⚠ Common exam trap
AWS often tests the distinction between orchestration features (like SageMaker Pipelines) and infrastructure management features (like auto-scaling), leading candidates to confuse pipeline benefits with SageMaker's broader managed service capabilities.
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
✓
Model lineage tracking from raw data to trained model artifacts.
SageMaker Pipelines automatically captures and tracks the lineage of every artifact, including datasets, processing jobs, training jobs, and model versions. This lineage is stored in SageMaker's metadata store, enabling full traceability from raw data to the final model artifact, which is critical for auditability and compliance in ML workflows.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Model lineage tracking from raw data to trained model artifacts.
Why this is correct
Pipelines automatically capture lineage metadata.
- ✗
Automated deployment of models to endpoints upon pipeline completion.
Why it's wrong here
Pipelines do not automatically deploy; a separate step or trigger is needed.
- ✓
Event-driven execution when new data arrives in S3.
Why this is correct
Pipelines can be triggered by events like S3 PutObject via EventBridge.
- ✗
Automatic scaling of compute resources based on data volume.
Why it's wrong here
Scaling is not a feature of Pipelines; it is for the underlying compute resources (e.g., training jobs) but not automatic scaling like endpoints.
- ✓
Reproducible execution through a directed acyclic graph (DAG) of steps with re-run capabilities.
Why this is correct
The DAG structure ensures each run is consistent and can be re-executed.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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.