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 wants to train a model that predicts whether a customer will respond to a marketing offer (yes or no). The dataset includes features such as age, income, past purchase history, and the labeled outcome (responded or not responded) for previous customers. Which type of machine learning is this?
Correct. The model is trained on labeled data (known outcomes) to predict a discrete class, making it a supervised classification task.
B
Unsupervised learning
Why wrong: Incorrect. Unsupervised learning is used when the data has no labels and the goal is to discover hidden patterns or groupings. Here, labels are provided.
C
Reinforcement learning
Why wrong: Incorrect. Reinforcement learning involves an agent learning through rewards and punishments in an environment, not from a fixed labeled dataset.
D
Semi-supervised learning
Why wrong: Incorrect. Semi-supervised learning uses a mix of labeled and unlabeled data. The scenario explicitly states that all previous customers have labeled outcomes.
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Supervised learning
This is supervised learning because the dataset includes labeled outcomes (responded or not responded) for previous customers, which the model uses to learn a mapping from input features (age, income, past purchase history) to the correct output. The goal is to predict a categorical label (yes/no), making it a classification task within supervised learning.
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. The model is trained on labeled data (known outcomes) to predict a discrete class, making it a supervised classification task.
Related concept
Read the scenario before looking for a memorised answer.
✗
Unsupervised learning
Why it's wrong here
Incorrect. Unsupervised learning is used when the data has no labels and the goal is to discover hidden patterns or groupings. Here, labels are provided.
✗
Reinforcement learning
Why it's wrong here
Incorrect. Reinforcement learning involves an agent learning through rewards and punishments in an environment, not from a fixed labeled dataset.
✗
Semi-supervised learning
Why it's wrong here
Incorrect. Semi-supervised learning uses a mix of labeled and unlabeled data. The scenario explicitly states that all previous customers have labeled outcomes.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates might confuse supervised learning with unsupervised learning, thinking that because the dataset has many features (age, income, etc.) it must be unsupervised clustering, but the presence of labeled outcomes clearly indicates supervised classification.
Trap categories for this question
Scenario analysis trap
Incorrect. Semi-supervised learning uses a mix of labeled and unlabeled data. The scenario explicitly states that all previous customers have labeled outcomes.
Detailed technical explanation
How to think about this question
Under the hood, supervised learning algorithms like logistic regression or decision trees minimize a loss function (e.g., cross-entropy for classification) by adjusting model weights based on the difference between predicted and actual labels. In Azure Machine Learning, this would be implemented using a classification pipeline with automated featurization and hyperparameter tuning. A real-world scenario is a marketing campaign where the model is trained on historical customer responses to predict which new customers are most likely to convert, enabling targeted offers.
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.
Related glossary terms
Concepts from this question explained
These glossary pages explain the core terms tested in this AI-900 question in full detail.
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 — This is supervised learning because the dataset includes labeled outcomes (responded or not responded) for previous customers, which the model uses to learn a mapping from input features (age, income, past purchase history) to the correct output. The goal is to predict a categorical label (yes/no), making it a classification task within supervised learning.
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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Question Discussion
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