AI-900 Practice Question: Describe fundamental principles of machine learning on Azure
What is 'semi-supervised learning' and when is it useful?
⚠ Common exam trap
Many 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.
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.
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
This confuses data labeling logistics with the learning paradigm. Semi-supervised learning is defined by the mix of training samples - a small set carrying ground-truth labels plus a much larger set with no labels - not by how many human annotators produce those labels. Having two humans split the labelling workload is an inter-annotator process concern, and any disagreement between them would be measured by inter-annotator agreement, not by changing the algorithm.
- ✓
Using small amounts of labelled data alongside large amounts of unlabelled data to train a model
Why this is correct
This is the core definition of semi-supervised learning. The model first learns patterns from the abundant unlabeled examples, then uses the scarce labeled examples to anchor those patterns to the correct output classes, often through self-training or pseudo-labelling. It is especially valuable when labelling is expensive, because unlabeled data is usually plentiful and cheap, letting the model improve decision boundaries without fully labeled datasets.
- ✗
A model that receives feedback from users during deployment to improve over time
Why it's wrong here
This describes a learning loop that takes place after deployment, closer to online learning or reinforcement learning, where new feedback becomes the reward signal. Semi-supervised learning, in contrast, is a training-time strategy over a fixed corpus; it does not require user interaction or real-time reward. User feedback in production could be used later for re-training or active learning, but that is not semi-supervised learning.
- ✗
Training that automatically stops halfway through and resumes the next day
Why it's wrong here
This is about checkpointing, resumable training jobs, and infrastructure scheduling, not about the data composition a learning algorithm sees. Semi-supervised learning refers purely to the ratio of labeled to unlabeled examples in the training set, and has nothing to do with whether training is paused or resumed. A model can be trained overnight, interrupted, or resumed, and still be either fully supervised, unsupervised, or semi-supervised based solely on the data.
Go deeper
Related to this question
Learn chapter
Machine Learning Core Concepts
Key term
Supervised learning
Supervised learning is a type of machine learning where a model learns from labeled training data to make predictions or decisions.
Key term
Model
In IT and AI, a model is a trained mathematical representation that learns patterns from data to make predictions or decisions.
About these practice questions
Courseiva writes every AI-900 question from scratch — 985 in total, each with an explanation and a wrong-answer breakdown. None are copied from real exams or dumps. Learn why practice questions differ from exam dumps →
JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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.