- A
When training data is accidentally deleted from storage
Why wrong: Data deletion is a storage incident — data drift is the gradual shift in data distribution over time that degrades model performance.
- B
When production data distribution changes from the training data distribution over time
Data drift occurs when real-world data patterns shift away from what the model was trained on, degrading prediction accuracy.
- C
When a model's weights change during inference
Why wrong: Model weights are fixed after training — data drift is about changes in the input data patterns, not model parameters.
- D
When data is moved between different Azure storage accounts
Why wrong: Storage movement is data migration — data drift is a statistical property change in production data over time.
Quick Answer
The correct answer is that data drift in machine learning occurs when the statistical distribution of the production data a deployed model receives changes over time, diverging from the distribution of the training data. This is the correct definition because a model learns patterns from its training data, and if the real-world input data shifts—such as a customer base aging or seasonal buying habits changing—the model’s predictions become less accurate even though the model itself hasn’t changed. On the Microsoft Azure AI Fundamentals AI-900 exam, this concept tests your understanding of model monitoring and why retraining is necessary; a common trap is confusing data drift with concept drift, where the relationship between inputs and outputs changes instead. Remember the memory tip: “Data drift = input shift, concept drift = relationship shift.” For the exam, focus on the idea that data drift degrades model reliability over time, which Azure Machine Learning detects by comparing production data distributions against the training baseline.
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 data drift in the context of deployed machine learning models?
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.
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.
- ✗
When training data is accidentally deleted from storage
Why it's wrong here
Data deletion is a storage incident — data drift is the gradual shift in data distribution over time that degrades model performance.
- ✓
When production data distribution changes from the training data distribution over time
Why this is correct
Data drift occurs when real-world data patterns shift away from what the model was trained on, degrading prediction accuracy.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
When a model's weights change during inference
Why it's wrong here
Model weights are fixed after training — data drift is about changes in the input data patterns, not model parameters.
- ✗
When data is moved between different Azure storage accounts
Why it's wrong here
Storage movement is data migration — data drift is a statistical property change in production data over time.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates 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.
Detailed technical explanation
How to think about this question
Under the hood, data drift is typically detected by comparing feature distributions using statistical tests like the Kolmogorov-Smirnov test for numerical features or the Chi-squared test for categorical features. Azure Machine Learning's data drift monitoring computes these metrics over sliding windows of production data and triggers alerts when drift exceeds a defined threshold. A real-world scenario is a retail demand forecasting model that fails after a holiday season because customer purchasing patterns shift, causing the input features (e.g., time of day, product categories) to drift from the training distribution.
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.
- →
Describe fundamental principles of machine learning on Azure — study guide chapter
Learn the concepts, then practise the questions
- →
Describe fundamental principles of machine learning on Azure practice questions
Targeted practice on this topic area only
- →
All AI-900 questions
1,020 questions across all exam domains
- →
Microsoft Azure AI Fundamentals AI-900 study guide
Full concept coverage aligned to exam objectives
- →
AI-900 practice test guide
How to use practice tests most effectively before exam day
Related practice questions
Related AI-900 practice-question pages
Use these pages to review the topic behind this question. This is how one missed question becomes focused revision.
Describe Artificial Intelligence workloads and considerations practice questions
Practise AI-900 questions linked to Describe Artificial Intelligence workloads and considerations.
Describe fundamental principles of machine learning on Azure practice questions
Practise AI-900 questions linked to Describe fundamental principles of machine learning on Azure.
Describe features of computer vision workloads on Azure practice questions
Practise AI-900 questions linked to Describe features of computer vision workloads on Azure.
Describe features of Natural Language Processing workloads on Azure practice questions
Practise AI-900 questions linked to Describe features of Natural Language Processing workloads on Azure.
Describe features of generative AI workloads on Azure practice questions
Practise AI-900 questions linked to Describe features of generative AI workloads on Azure.
AI-900 fundamentals practice questions
Practise AI-900 questions linked to AI-900 fundamentals.
AI-900 scenario practice questions
Practise AI-900 questions linked to AI-900 scenario.
AI-900 troubleshooting practice questions
Practise AI-900 questions linked to AI-900 troubleshooting.
Practice this exam
Start a free AI-900 practice session
Short sessions build daily habit. Longer sessions build exam-day stamina. Try a timed session to simulate real conditions.
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: 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.
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
Courseiva creates original exam-style practice questions with explanations and wrong-answer analysis. It does not publish real exam questions, exam dumps, or protected exam content. Learn why practice questions differ from exam dumps →
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.
Question Discussion
Share a tip, memory trick, or ask about the reasoning behind this question. Do not post real exam questions, leaked content, braindumps, or copyrighted exam material. Comments are moderated and may be removed without notice.
Sign in to join the discussion.