- A
Having users actively participate in model training by rating AI responses
Why wrong: User rating of responses is RLHF — active learning selects which unlabelled examples are most valuable for human annotation.
- B
Strategically selecting the most informative examples for human labelling to maximise learning efficiency
Active learning labels uncertain model predictions first — achieving better performance with fewer labels than random selection.
- C
A training approach where the model actively searches the internet for additional training data
Why wrong: Web data scraping is data collection — active learning is a strategy for prioritising which unlabelled examples to label next.
- D
Continuous model training that runs actively in the background as new data arrives
Why wrong: Continuous training is an online learning architecture — active learning is a labelling strategy for batch annotation workflows.
Quick Answer
The answer is strategically selecting the most informative examples for human labelling to maximise learning efficiency. This is correct because active learning in Azure Machine Learning data labelling works by having the model identify the data points it is least confident about, such as those near decision boundaries, and prioritizing those specific examples for human review. By focusing on these uncertain instances, each labelled example provides the highest possible information gain, which reduces the total number of labels needed while still training a robust model. On the Microsoft Azure AI Fundamentals AI-900 exam, this concept tests your understanding of how Azure optimizes the labelling process to save time and cost; a common trap is confusing active learning with random sampling or passive labelling. Remember the memory tip: "Uncertainty equals efficiency"—if the model is unsure, it asks for help, making every label count.
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 'active learning' in Azure Machine Learning data labelling?
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
Strategically selecting the most informative examples for human labelling to maximise learning efficiency
Active learning in Azure Machine Learning data labelling is a technique where the model identifies the data points it is most uncertain about and prioritizes those for human review. This strategic selection maximizes the learning efficiency of the model by ensuring that each labelled example provides the highest possible information gain, reducing the total number of labels needed.
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.
- ✗
Having users actively participate in model training by rating AI responses
Why it's wrong here
User rating of responses is RLHF — active learning selects which unlabelled examples are most valuable for human annotation.
- ✓
Strategically selecting the most informative examples for human labelling to maximise learning efficiency
Why this is correct
Active learning labels uncertain model predictions first — achieving better performance with fewer labels than random selection.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
A training approach where the model actively searches the internet for additional training data
Why it's wrong here
Web data scraping is data collection — active learning is a strategy for prioritising which unlabelled examples to label next.
- ✗
Continuous model training that runs actively in the background as new data arrives
Why it's wrong here
Continuous training is an online learning architecture — active learning is a labelling strategy for batch annotation workflows.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates confuse 'active learning' with 'online learning' or 'continuous training' (Option D), because both involve iterative model updates, but active learning is specifically about sample selection efficiency, not the timing of training.
Detailed technical explanation
How to think about this question
Under the hood, Azure Machine Learning's active learning uses uncertainty sampling, often based on the model's predicted probability scores (e.g., entropy or margin of confidence) to rank unlabelled instances. The system then sends only the top-k most uncertain samples to the labelling queue, which can dramatically reduce labelling costs—by up to 80% in some real-world scenarios—while maintaining model accuracy. This is particularly valuable in domains like medical imaging or document classification where labelled data is expensive to obtain.
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 cloud solutions architect for a retail company is evaluating services for a new workload. The correct answer here reflects best practice for the specific scenario described — not a general cloud recommendation. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Cloud exam questions reward reading the constraint carefully: the same technology can be right or wrong depending on the use case.
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: Strategically selecting the most informative examples for human labelling to maximise learning efficiency — Active learning in Azure Machine Learning data labelling is a technique where the model identifies the data points it is most uncertain about and prioritizes those for human review. This strategic selection maximizes the learning efficiency of the model by ensuring that each labelled example provides the highest possible information gain, reducing the total number of labels needed.
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.
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Last reviewed: Jun 11, 2026
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