- A
Supervised learning
Correct because the dataset contains labeled images, which is the hallmark of supervised learning. The model learns from the labeled data to predict labels for new data.
- B
Unsupervised learning
Why wrong: Incorrect because unsupervised learning works with unlabeled data to find hidden patterns or groupings. Here, labels are available.
- C
Reinforcement learning
Why wrong: Incorrect because reinforcement learning involves an agent learning through rewards and punishments by interacting with an environment. This scenario does not involve an environment or reward signals.
- D
Semi-supervised learning
Why wrong: Incorrect because semi-supervised learning uses a small amount of labeled data and a large amount of unlabeled data. The dataset here is fully labeled, so supervised learning is more appropriate.
Quick Answer
The answer is supervised learning because the dataset contains labeled images of cats and dogs, and the goal is to classify new unlabeled images into those predefined categories. Supervised learning algorithms, such as convolutional neural networks, learn a mapping from input features—like pixel values—to output labels using the provided ground-truth tags, enabling accurate classification on unseen data. On the Microsoft Azure AI Fundamentals AI-900 exam, this scenario tests your understanding of when to apply supervised learning versus unsupervised or reinforcement learning, often appearing as a straightforward scenario where labeled data drives prediction. A common trap is confusing supervised learning with unsupervised learning, which would require unlabeled data to find hidden patterns rather than predict known categories. Remember the memory tip: “Supervised = Supervised by labels” — if your data comes with answers, you’re doing supervised learning.
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. 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.
A data scientist has a dataset containing thousands of labeled images of cats and dogs. The data scientist wants to train a model that can automatically classify new unlabeled images as either 'cat' or 'dog'. Which type of machine learning should the data scientist use?
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
Supervised learning
The correct answer is A, supervised learning, because the dataset contains labeled images (each image is tagged as 'cat' or 'dog'), and the goal is to train a model to predict the label for new unlabeled images. Supervised learning algorithms, such as convolutional neural networks (CNNs), learn a mapping from input features (pixel values) to output labels using the provided ground-truth labels, enabling accurate classification on unseen data.
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.
- ✓
Supervised learning
Why this is correct
Correct because the dataset contains labeled images, which is the hallmark of supervised learning. The model learns from the labeled data to predict labels for new data.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Unsupervised learning
Why it's wrong here
Incorrect because unsupervised learning works with unlabeled data to find hidden patterns or groupings. Here, labels are available.
- ✗
Reinforcement learning
Why it's wrong here
Incorrect because reinforcement learning involves an agent learning through rewards and punishments by interacting with an environment. This scenario does not involve an environment or reward signals.
- ✗
Semi-supervised learning
Why it's wrong here
Incorrect because semi-supervised learning uses a small amount of labeled data and a large amount of unlabeled data. The dataset here is fully labeled, so supervised learning is more appropriate.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates may confuse 'semi-supervised learning' with 'supervised learning' when they see a large labeled dataset, but semi-supervised learning is only appropriate when labeled data is scarce, not when thousands of labeled examples are already available.
Trap categories for this question
Scenario analysis trap
Incorrect because reinforcement learning involves an agent learning through rewards and punishments by interacting with an environment. This scenario does not involve an environment or reward signals.
Detailed technical explanation
How to think about this question
Under the hood, supervised learning for image classification typically uses a deep neural network like a CNN, where the model learns hierarchical feature representations (edges, textures, shapes) through backpropagation and gradient descent. The labeled dataset is split into training, validation, and test sets to tune hyperparameters and evaluate generalization. In real-world scenarios, this approach is used in Azure Custom Vision or AutoML for Image, where you upload labeled images and the service automatically trains a classification model.
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 healthcare organisation deploys an application with a public-facing web tier and a private database tier. The database subnet has no public IP and only accepts connections from the web tier's security group. Questions like this test whether you can design cloud network isolation using VNets/VPCs, subnets, and security group rules.
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: Supervised learning — The correct answer is A, supervised learning, because the dataset contains labeled images (each image is tagged as 'cat' or 'dog'), and the goal is to train a model to predict the label for new unlabeled images. Supervised learning algorithms, such as convolutional neural networks (CNNs), learn a mapping from input features (pixel values) to output labels using the provided ground-truth labels, enabling accurate classification on unseen data.
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
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.
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