MLS-C01 Practice Question: Machine Learning Implementation and Operations
A company is using Amazon SageMaker to build a machine learning pipeline. The pipeline includes data preprocessing, training, and evaluation steps. The company wants to ensure that the pipeline is reproducible and that artifacts are versioned. Which TWO actions should be taken? (Choose TWO.)
⚠ Common exam trap
Watch out — candidates often confuse data versioning (Option E) with pipeline versioning, or assume that a naming convention (Option A) or CI/CD trigger (Option D) is sufficient for reproducibility, when in fact only a purpose-built pipeline orchestration service with artifact versioning (Option B) combined with environment pinning (Option C) meets both requirements.
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
✓
Use SageMaker Pipelines to create the pipeline and enable versioning on the pipeline artifacts.
SageMaker Pipelines provides a native way to define, orchestrate, and version machine learning pipelines. By enabling versioning on pipeline artifacts (e.g., via the `Pipeline` object's `version` parameter or by using SageMaker Model Registry), each pipeline run is tracked with a unique version, ensuring reproducibility. This directly addresses the requirement for reproducible pipelines and versioned artifacts.
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 naming convention for training jobs that includes the date.
Why it's wrong here
Does not version artifacts automatically.
- ✓
Use SageMaker Pipelines to create the pipeline and enable versioning on the pipeline artifacts.
Why this is correct
SageMaker Pipelines version artifacts automatically.
- ✓
Create a requirements.txt file with specific library versions for the training script.
Why this is correct
Pins dependencies for reproducibility.
- ✗
Use AWS CodePipeline to trigger the pipeline on code changes.
Why it's wrong here
Automates triggers but does not version artifacts.
- ✗
Store the training dataset in a versioned S3 bucket.
Why it's wrong here
Versions data, not pipeline artifacts.
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