AI-900 Practice Question: Describe fundamental principles of machine learning on Azure
A data scientist has a dataset containing images of handwritten digits (0-9) where each image is labeled with the correct digit. The goal is to train a model that can predict the digit from a new image. Which type of machine learning approach should be used?
⚠ Common exam trap
Test-takers frequently confuse regression with classification when the output is a number (0-9), but regression is for continuous values, not discrete labels, even if the labels are numeric.
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
✓
Classification
This is a supervised learning problem where the model must predict a discrete class label (digit 0-9) from input images. Classification algorithms, such as logistic regression or neural networks, are designed to map inputs to categorical outputs, making B the correct choice.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Regression
Why it's wrong here
Regression predicts numeric continuous values (e.g., price, temperature), not discrete class labels. Handwritten digit recognition outputs a categorical label from 0-9, which is a classification task. Even if a model outputs a numeric value, the target is a class membership, not a quantity with meaningful arithmetic.
- ✓
Classification
Why this is correct
Classification is a supervised learning technique that learns a decision boundary from labeled examples to assign new inputs to discrete categories. Recognizing handwritten digits is a classic multiclass classification problem where each image maps to one of ten classes. The model is trained on images with known digit labels to generalize to unseen handwriting.
- ✗
Clustering
Why it's wrong here
Clustering is an unsupervised method that discovers natural groupings in data without pre-existing labels. It would group similar images based on pixel patterns, but cannot assign a digit name because it never sees ground-truth labels during training. The goal here is to predict a specific known label, not to find hidden structure.
- ✗
Reinforcement learning
Why it's wrong here
Reinforcement learning involves an agent learning a policy through trial-and-error interactions with an environment, receiving rewards or penalties. There is no sequential decision-making or reward signal in handwritten digit classification—each image is independent and the correct label is known in the training set. Thus RL is not applicable.
Go deeper
Related to this question
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Machine Learning Core Concepts
Key term
Classification
Classification is a supervised machine learning technique used to predict a category or class label for new data based on patterns learned from labeled training data.
Key term
Machine learning
Machine learning is a branch of artificial intelligence where computers learn patterns from data to make decisions or predictions without being explicitly programmed for every task.
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