AI-900 Practice Question: Describe fundamental principles of machine learning on Azure
What is data drift in the context of deployed machine learning models?
⚠ Common exam trap
Many exam-takers confuse data drift with other operational issues like data loss or storage changes, rather than recognizing it as a statistical shift in the input data distribution that degrades model accuracy over time.
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
✓
When production data distribution changes from the training data distribution over time
Data drift refers to the phenomenon where the statistical properties of the input data a deployed model receives in production change over time, diverging from the distribution of the data used during training. This degradation can cause the model's predictions to become less accurate or unreliable, even if the model itself remains unchanged. In Azure Machine Learning, data drift is monitored using dataset monitors that compare production data distributions against the training baseline.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
When training data is accidentally deleted from storage
Why it's wrong here
Accidentally deleting training data is an operational or storage incident, not a statistical phenomenon. Data drift specifically refers to a gradual shift in the probability distribution of production input features relative to the training distribution, often due to evolving real-world conditions. Deletion may prevent retraining or auditing, but it does not by itself alter the distributions that the deployed model observes.
- ✓
When production data distribution changes from the training data distribution over time
Why this is correct
Data drift occurs when the distribution of features observed in production gradually diverges from the distribution seen during training, such as when customer demographics, economic conditions, or sensor readings change over time. Because the model learned patterns from historical data, this input distribution shift causes predictions to become less accurate even though the model's logic remains unchanged. Monitoring this divergence is essential for triggering retraining.
- ✗
When a model's weights change during inference
Why it's wrong here
Model weights are fixed after training concludes; during inference, the deployed model only performs forward propagation using those static weights to produce predictions. If weights were modified during inference, that would be online learning or a model update, not data drift, which relates entirely to the input data's statistical properties, not the model's internal parameters. Confusing parameter changes with data drift conflates model state with data distribution.
- ✗
When data is moved between different Azure storage accounts
Why it's wrong here
Moving data between Azure storage accounts is a data migration operation that changes the physical location and maybe the access pathway, but it does not change the underlying statistical distribution of the data. Data drift is about changes in the meaning or distribution of values — such as a seasonal shift in sales or a change in user behavior — not about where files are stored. While migration could cause availability issues, it has no direct effect on the model's input feature distribution.
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Related to this question
Learn chapter
Machine Learning Core Concepts
Key term
Machine learning
Machine learning is a branch of artificial intelligence where computers learn patterns from data to make decisions or predictions without being explicitly programmed for every task.
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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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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