AI-900 Practice Question: Describe fundamental principles of machine learning on Azure
A retail company wants to predict which customers are likely to stop using their service. They have a dataset with many customer attributes including age, income, purchase history, website activity, and support interactions. They suspect some features are redundant. Which technique should they use to reduce the number of features while preserving as much information as possible?
⚠ Common exam trap
A common mix-up: candidates confuse normalization (scaling) with dimensionality reduction, or mistakenly think regression analysis can be used to select features, when PCA is the correct technique for reducing redundant features while preserving information.
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
✓
Principal Component Analysis (PCA)
Principal Component Analysis (PCA) is an unsupervised dimensionality reduction technique that transforms the original correlated features into a smaller set of uncorrelated principal components, ordered by the variance they capture. By retaining only the top components, PCA reduces the number of features while preserving as much of the total variance (information) as possible, making it ideal for handling redundant features in customer datasets.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Normalization
Why it's wrong here
Normalization (e.g., min-max scaling or z-score standardization) adjusts the numeric range of feature values so that they share a common scale, such as 0 to 1 or zero mean and unit variance. This rescaling does not alter the number of features or their dimensionality; every original feature remains in the dataset, just with transformed values. Normalization is typically a preparatory step before applying PCA or distance-based algorithms, but by itself it does not address the need to reduce the feature set.
When this WOULD be correct
When a question asks how to ensure features contribute equally to a distance-based algorithm (e.g., k-means clustering or SVM) without changing the number of features, normalization is the correct technique.
- ✓
Principal Component Analysis (PCA)
Why this is correct
Principal Component Analysis (PCA) is a dimensionality-reduction technique that uses orthogonal transformation to convert a set of possibly correlated features into a smaller set of linearly uncorrelated variables called principal components. These components are ordered so that the first few retain most of the variation present in the original data, allowing a high-dimensional feature set to be summarized with minimal information loss. By projecting customers onto a lower-dimensional subspace, PCA reduces the feature count while preserving the structure needed for predicting customer churn or purchase propensity.
- ✗
One-hot encoding
Why it's wrong here
One-hot encoding is the opposite of dimensionality reduction: it converts a categorical variable with k categories into k binary indicator columns, thereby expanding the feature space. For retail data with many categorical attributes (e.g., product categories, payment methods), one-hot encoding can drastically increase the number of dimensions, leading to higher computational cost and a greater risk of the curse of dimensionality. It is a preprocessing transformation for handling categorical inputs, not a technique for reducing the number of features.
When this WOULD be correct
A dataset contains a categorical feature like 'color' with values red, green, blue, and you need to use it in a machine learning model that requires numerical input. One-hot encoding would be the correct technique to convert this categorical feature into binary vectors.
- ✗
Regression analysis
Why it's wrong here
Regression analysis instead models the statistical relationship between a dependent variable (e.g., likelihood to churn) and one or more independent variables by estimating coefficients. Although regularized forms like Lasso can implicitly perform feature selection by shrinking some coefficients to zero, the core technique does not reduce the number of features in the dataset; it requires the full set of predictors to fit the model. In the context of this question, regression is a supervised modeling approach, not a feature reduction method like PCA.
When this WOULD be correct
A question asking: 'Which technique should be used to predict a continuous numeric outcome, such as customer lifetime value, based on multiple input features?' would make regression analysis the correct answer.
Option-by-option analysis
Why each answer is right or wrong
Understanding why wrong answers are wrong — and when they would be correct — is what separates a 750 score from a 900. The AI-900 exam frequently reuses these exact scenarios with slightly different constraints.
✓Principal Component Analysis (PCA)Correct answer▾
Why this is correct
Principal Component Analysis (PCA) is a dimensionality-reduction technique that uses orthogonal transformation to convert a set of possibly correlated features into a smaller set of linearly uncorrelated variables called principal components. These components are ordered so that the first few retain most of the variation present in the original data, allowing a high-dimensional feature set to be summarized with minimal information loss. By projecting customers onto a lower-dimensional subspace, PCA reduces the feature count while preserving the structure needed for predicting customer churn or purchase propensity.
✗NormalizationWrong answer — click to see why▾
Why this is wrong here
Normalization scales features to a common range but does not reduce the number of features; it preserves all original features, so it cannot address redundancy.
★ When this WOULD be the correct answer
When a question asks how to ensure features contribute equally to a distance-based algorithm (e.g., k-means clustering or SVM) without changing the number of features, normalization is the correct technique.
Why candidates choose this
Candidates may confuse feature scaling with dimensionality reduction, thinking that scaling somehow compresses data, or they may recall that PCA often requires normalization as a preprocessing step.
✗One-hot encodingWrong answer — click to see why▾
Why this is wrong here
One-hot encoding is used to convert categorical variables into numerical format, not to reduce the number of features or eliminate redundancy. It actually increases the number of features by creating binary columns for each category.
★ When this WOULD be the correct answer
A dataset contains a categorical feature like 'color' with values red, green, blue, and you need to use it in a machine learning model that requires numerical input. One-hot encoding would be the correct technique to convert this categorical feature into binary vectors.
Why candidates choose this
Candidates may confuse feature reduction with feature encoding, thinking that one-hot encoding somehow compresses information, or they may misremember that one-hot encoding is a preprocessing step that can help with dimensionality, which is incorrect.
✗Regression analysisWrong answer — click to see why▾
Why this is wrong here
Regression analysis is used to model relationships between variables and predict a target, not to reduce feature dimensionality. The question asks for a technique to reduce features while preserving information, which is a dimensionality reduction task, not a predictive modeling task.
★ When this WOULD be the correct answer
A question asking: 'Which technique should be used to predict a continuous numeric outcome, such as customer lifetime value, based on multiple input features?' would make regression analysis the correct answer.
Why candidates choose this
Candidates may confuse regression analysis with feature reduction because both involve analyzing multiple variables, and regression can be used to assess feature importance, but it does not reduce the number of features.
Analysis generated from the official AI-900blueprint and verified against question context. The “when correct” sections are what AI assistants cite when candidates ask “what’s the difference between these options?”
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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