- A
The family tree of model architectures showing which models inspired the design
Why wrong: Architecture inspiration is research history — model lineage tracks the specific training run, data, and code that produced a specific model version.
- B
A tracked history of the dataset, code, hyperparameters, and compute used to produce a model
Lineage enables complete reproduction and audit — tracking every input that produced each model version for debugging and compliance.
- C
The geographic lineage of training data showing which regions it was collected from
Why wrong: Data geography is a data governance concern — model lineage tracks the technical inputs (data version, code, compute) that produced the model.
- D
The sequence of model versions deployed to production over time
Why wrong: Deployment history is a release management record — lineage is about the provenance of how each model version was trained.
Quick Answer
The correct answer is a tracked history of the dataset, code, hyperparameters, and compute used to produce a model. This is correct because model lineage in Azure Machine Learning captures the complete lifecycle of a model, linking every training run to its specific inputs and environment, which ensures full reproducibility and auditability. On the Microsoft Azure AI Fundamentals AI-900 exam, this concept tests your understanding of governance and traceability within MLOps, often appearing as a scenario where you must identify which artifacts are logged to recreate a model. A common trap is confusing model lineage with simple version control—lineage includes not just the model file but the entire context of its creation, including compute targets and hyperparameter values. Remember the memory tip: "Lineage links the life story—data, code, compute, and config—so you can replay the training glory."
AI-900 Practice Question: Describe fundamental principles of machine learning on Azure
This AI-900 practice question tests your understanding of describe fundamental principles of machine learning on azure. Match the stated requirement to the specific cloud service, access model, or configuration option — many options are valid in isolation but not for this scenario. 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.
What is 'model lineage' in Azure Machine Learning?
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.
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.
- ✗
The family tree of model architectures showing which models inspired the design
Why it's wrong here
Architecture inspiration is research history — model lineage tracks the specific training run, data, and code that produced a specific model version.
- ✓
A tracked history of the dataset, code, hyperparameters, and compute used to produce a model
Why this is correct
Lineage enables complete reproduction and audit — tracking every input that produced each model version for debugging and compliance.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
The geographic lineage of training data showing which regions it was collected from
Why it's wrong here
Data geography is a data governance concern — model lineage tracks the technical inputs (data version, code, compute) that produced the model.
- ✗
The sequence of model versions deployed to production over time
Why it's wrong here
Deployment history is a release management record — lineage is about the provenance of how each model version was trained.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that 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.
Detailed technical explanation
How to think about this question
Under the hood, Azure Machine Learning implements model lineage by associating each registered model with a specific run ID in the workspace, which stores snapshots of the source code, input datasets (via dataset IDs), hyperparameter values, and compute target details (e.g., VM size, cluster name). This is stored in the Azure ML metadata store and can be queried via the SDK or CLI, enabling full traceability even after the model is deployed. In a real-world scenario, if a model in production produces biased predictions, lineage allows auditors to pinpoint the exact training dataset version and hyperparameter configuration that caused the issue, facilitating rapid remediation.
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
An e-commerce site experiences heavy traffic on Black Friday and near-zero traffic during off-peak weeks. Rather than provisioning permanent large VMs, the team uses auto-scaling groups that add capacity automatically under load and reduce it overnight. Questions like this test whether you understand elasticity, availability zones, and cloud compute scaling patterns.
What to study next
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FAQ
Questions learners often ask
What does this AI-900 question test?
Describe fundamental principles of machine learning on Azure — This question tests Describe fundamental principles of machine learning on Azure — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: 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.
What should I do if I get this AI-900 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.
About these practice questions
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Last reviewed: Jun 11, 2026
This AI-900 practice question is part of Courseiva's free Microsoft 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 AI-900 exam.
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