RegisterModel Step for Lineage Tracking
A team uses SageMaker Pipelines with a Condition step to decide whether to register a model based on evaluation metrics. They want to also store the evaluation results for lineage tracking. Which step should they use to record the metrics?
Quick Answer
The requirement here is narrow but specific: after a Condition step decides a model is good enough, the team needs somewhere to persist both the model artifact and the evaluation numbers that justified registering it, so future audits or rollbacks can see why a version was approved. The RegisterModel step is built for exactly this handoff point in a SageMaker Pipeline, creating a versioned entry in the Model Registry and accepting metadata, including evaluation metrics, alongside the model itself, so the metrics become part of the model's permanent record rather than living only in a transient processing job output. This is different from an earlier evaluation step in the pipeline, which computes the metrics but doesn't attach them to a registry entry, and different from the Condition step, which only branches execution based on the metrics without storing anything, meaning neither of those steps alone satisfies the lineage requirement. The RegisterModel step is the one place in the pipeline where the fact that a model passed evaluation with specific numbers becomes a durable, queryable record tied to that model version. Whenever a pipeline question asks specifically about recording or tracing evaluation results for a registered model, the RegisterModel step and its metadata capability is the mechanism being tested.
⚠ Common exam trap
It's easy for candidates to assume the Condition step or Processing step can directly store metrics for lineage, but only the RegisterModel step can bind evaluation results to a model version in the Model Registry, which is the explicit requirement for lineage tracking.
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
✓
RegisterModel step
The RegisterModel step in SageMaker Pipelines is designed to create a model version in the SageMaker Model Registry, and it can accept metadata such as evaluation metrics via the `InferenceSpecification` or by passing a metrics dictionary. This allows the team to store evaluation results alongside the model for lineage tracking, fulfilling the requirement to record metrics after a Condition step approves registration.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Condition step
Why it's wrong here
Condition step controls branching but does not record metrics.
- ✗
Training step
Why it's wrong here
Training step outputs a model artifact but does not record evaluation metrics.
- ✓
RegisterModel step
Why this is correct
RegisterModel step registers the model and can include evaluation metrics as metadata.
- ✗
Processing step
Why it's wrong here
Processing steps run custom code but do not inherently store metrics in the registry.
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Same concept, more angles
1 more way this is tested on MLA-C01
These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.
Variation 1. A data scientist wants to train a model on SageMaker using a custom PyTorch script, then register the best model in the SageMaker Model Registry. The training job is part of a SageMaker Pipeline. Which pipeline step should be used to register the model?
medium- ✓ A.RegisterModelStep
- B.CreateModelStep
- C.TrainingStep
- D.TransformStep
Why A: The `RegisterModelStep` is specifically designed to create a model resource and register it in the SageMaker Model Registry as part of a pipeline. It takes the training output (e.g., model artifacts from a `TrainingStep`) and packages it with the specified inference image and metadata, then creates a model package group version. This is the correct step for registering a model after training, as it directly integrates with the Model Registry for versioning and approval workflows.
JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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