The answer is to enable versioning on pipeline artifacts and create a requirements.txt file with specific library versions for the training script. These two actions ensure a reproducible SageMaker pipeline with versioning by locking both the code dependencies and the pipeline’s execution history. SageMaker Pipelines natively supports artifact versioning through the Pipeline object’s version parameter or the Model Registry, which tags each run with a unique identifier, while pinning library versions in requirements.txt prevents environment drift that could alter results. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this question tests your understanding of MLOps best practices for reproducibility—a common trap is choosing only one of these actions, such as versioning artifacts but neglecting dependency pinning, or vice versa. Remember the mnemonic “Lock and Tag”: lock your dependencies with requirements.txt, and tag your pipeline runs with versioning.
MLS-C01 Practice Question: Machine Learning Implementation and Operations
This MLS-C01 practice question tests your understanding of machine learning implementation and operations. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
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.)
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.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
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.
Related concept
Read the scenario before looking for a memorised answer.
✓
Create a requirements.txt file with specific library versions for the training script.
Why this is correct
Pins dependencies for reproducibility.
Related concept
Read the scenario before looking for a memorised answer.
✗
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.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that 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.
Detailed technical explanation
How to think about this question
SageMaker Pipelines uses a directed acyclic graph (DAG) of steps, each defined by a `ProcessingStep`, `TrainingStep`, or `TransformStep`. When you create a pipeline, you can specify a `PipelineExperimentConfig` to automatically log parameters and metrics to Amazon SageMaker Experiments, which ties each run to a specific version of the pipeline definition. Under the hood, SageMaker stores the pipeline definition as a JSON file in an S3 bucket, and enabling versioning on that bucket (or using the built-in versioning of the `Pipeline` object) ensures that every change to the pipeline is tracked, allowing full rollback and auditability.
KKey Concepts to Remember
Read the scenario before looking for a memorised answer.
Find the constraint that changes the correct option.
Eliminate answers that are true in general but not in this case.
TExam Day Tips
→Watch for words such as best, first, most likely and least administrative effort.
→Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
A media company stores terabytes of video archives that are accessed once a year for audit purposes. Moving these objects to a cold storage tier (Azure Archive, S3 Glacier, or Google Nearline) costs a fraction of hot storage. Questions like this test whether you understand storage tiers, access frequency tradeoffs, and retrieval latency requirements.
What to study next
Got this wrong? Here's your next step.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Machine Learning Implementation and Operations — This question tests Machine Learning Implementation and Operations — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: 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.
What should I do if I get this MLS-C01 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Question Discussion
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