- A
Training a model that is partially supervised by one human and partially by another
Why wrong: Multiple annotators is a labelling workflow concern — semi-supervised learning combines small labelled sets with large unlabelled datasets.
- B
Using small amounts of labelled data alongside large amounts of unlabelled data to train a model
Semi-supervised learning leverages unlabelled data (cheap to collect) with scarce labels — useful when annotation is expensive.
- C
A model that receives feedback from users during deployment to improve over time
Why wrong: Online learning from user feedback is reinforcement learning — semi-supervised uses both labelled and unlabelled data at training time.
- D
Training that automatically stops halfway through and resumes the next day
Why wrong: Interrupted training is a compute scheduling issue — semi-supervised learning describes the ratio of labelled to unlabelled training data.
What Is Semi-Supervised Learning?
This AI-900 practice question tests your understanding of describe fundamental principles of machine learning on azure. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. 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 'semi-supervised learning' and when is it useful?
Quick Answer
The correct answer is that semi-supervised learning uses small amounts of labelled data alongside large amounts of unlabelled data to train a model. This approach is effective because the model first learns underlying patterns from the limited labelled subset, then applies those patterns to propagate labels across the larger unlabelled dataset, iteratively refining its predictions. On the Microsoft Azure AI Fundamentals AI-900 exam, this concept tests your understanding of when to choose semi-supervised learning over supervised or unsupervised methods, often appearing in scenario-based questions about cost-effective model training. A common trap is confusing it with self-supervised learning, but remember: semi-supervised leverages a tiny labelled seed to guide the unlabelled mass. Memory tip: think of it as a teacher giving a few example problems, then letting students solve hundreds of similar ones on their own.
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
Using small amounts of labelled data alongside large amounts of unlabelled data to train a model
Semi-supervised learning combines a small set of labeled data with a large set of unlabeled data to train a model. This approach is useful when labeling data is expensive or time-consuming, but large volumes of unlabeled data are readily available. The model first learns patterns from the labeled subset, then propagates those labels to the unlabeled data, iteratively improving its accuracy.
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.
- ✗
Training a model that is partially supervised by one human and partially by another
Why it's wrong here
Multiple annotators is a labelling workflow concern — semi-supervised learning combines small labelled sets with large unlabelled datasets.
- ✓
Using small amounts of labelled data alongside large amounts of unlabelled data to train a model
Why this is correct
Semi-supervised learning leverages unlabelled data (cheap to collect) with scarce labels — useful when annotation is expensive.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
A model that receives feedback from users during deployment to improve over time
Why it's wrong here
Online learning from user feedback is reinforcement learning — semi-supervised uses both labelled and unlabelled data at training time.
- ✗
Training that automatically stops halfway through and resumes the next day
Why it's wrong here
Interrupted training is a compute scheduling issue — semi-supervised learning describes the ratio of labelled to unlabelled training data.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates confuse semi-supervised learning with active learning or human-in-the-loop workflows, but the key differentiator is the use of both labeled and unlabeled data in the training process, not the number of humans or feedback loops.
Detailed technical explanation
How to think about this question
Under the hood, semi-supervised learning often uses techniques like self-training (pseudo-labeling) or co-training, where a model trained on the labeled data assigns high-confidence predictions to unlabeled examples, which are then added to the training set. A subtle behavior is that if the initial labeled data is biased, the pseudo-labels can reinforce errors, making careful selection of the labeled subset critical. In real-world scenarios like medical imaging, where expert labeling is scarce, semi-supervised learning can achieve near-supervised accuracy with only 5-10% of the data labeled.
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: Using small amounts of labelled data alongside large amounts of unlabelled data to train a model — Semi-supervised learning combines a small set of labeled data with a large set of unlabeled data to train a model. This approach is useful when labeling data is expensive or time-consuming, but large volumes of unlabeled data are readily available. The model first learns patterns from the labeled subset, then propagates those labels to the unlabeled data, iteratively improving its accuracy.
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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