AI-900 Practice Question: Describe fundamental principles of machine learning on Azure
What is 'model lineage' in Azure Machine Learning?
⚠ Common exam trap
A common mix-up: candidates confuse model lineage with simple versioning or deployment history, overlooking that it specifically includes the complete provenance of data, code, and compute used during training, not just the sequence of model versions.
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
✓
A tracked history of the dataset, code, hyperparameters, and compute used to produce a model
Model lineage in Azure Machine Learning is a tracked history that captures the complete lifecycle of a model, including the dataset, code, hyperparameters, and compute environment used to produce it. This is essential for reproducibility, auditability, and governance, as it allows data scientists to trace exactly how a model was trained and which artifacts were involved. Azure ML automatically logs this lineage through its run history and model registry, ensuring every model version is linked to its training run.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
The family tree of model architectures showing which models inspired the design
Why it's wrong here
Architecture lineage describes which prior neural network designs or research ideas influenced a model's structure, reflecting intellectual or research history rather than the concrete artifacts used to instantiate a trained model. In contrast, model lineage is a technical, run-specific record of the dataset, code, hyperparameters, and compute configuration that produced a particular trained version, which is necessary for reproducibility and compliance, not conceptual inspiration.
- ✓
A tracked history of the dataset, code, hyperparameters, and compute used to produce a model
Why this is correct
This is the correct definition: model lineage in MLOps platforms like Azure Machine Learning tracks the complete set of inputs—dataset version, code commit, hyperparameter values, and compute environment—that produced a specific model version, enabling exact reproduction, debugging, and regulatory audit. Because every training run is recorded, teams can trace a model's prediction back to its originating data and configuration, satisfying governance and reproducibility requirements.
- ✗
The geographic lineage of training data showing which regions it was collected from
Why it's wrong here
Geographic lineage answers where training data originated, which is a data governance and residency concern—covering compliance, privacy, and jurisdictional restrictions—rather than a record of the technical provenance that produced a model. True model lineage captures the exact dataset version, code revision, hyperparameter set, and compute environment used in a specific training run, not just the data's regional source.
- ✗
The sequence of model versions deployed to production over time
Why it's wrong here
Deployment history is an operational release record showing which model versions were promoted to production endpoints and when, but it omits the training-time inputs that define each version's provenance. Model lineage is the pre-deployment audit trail of the specific data snapshot, code commit, training parameters, and compute infrastructure that generated a given model artifact, so deployment chronology alone cannot reproduce or debug a model.
Go deeper
Related to this question
Learn chapter
Machine Learning Core Concepts
Key term
Trace
A trace is a record of the path and timing of a request or operation as it moves through components in a system, used to monitor performance and troubleshoot issues.
Key term
Azure Machine Learning
Azure Machine Learning is a cloud service for building, training, and deploying machine learning models at scale.
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