The correct technique is Principal Component Analysis (PCA), an unsupervised dimensionality reduction method that transforms correlated features into a smaller set of uncorrelated principal components ranked by the variance they capture. By retaining only the top components, PCA reduces the number of features while preserving as much of the total information as possible, making it ideal for handling redundant customer attributes like age, income, and purchase history. On the AI-900 exam, this question tests your understanding of when to apply unsupervised techniques for feature reduction versus supervised methods like feature selection; a common trap is confusing PCA with feature selection algorithms that drop features entirely rather than transforming them. Remember the memory tip: PCA is like packing a suitcase—you compress the most important variance into fewer dimensions without throwing away the essential content.
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 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?
Why wrong: Normalization scales features to a common range but does not reduce the number of features.
B
Principal Component Analysis (PCA)
PCA summarizes data by creating new uncorrelated variables (principal components) that capture most of the variance, effectively reducing dimensionality.
C
One-hot encoding
Why wrong: One-hot encoding converts categorical variables into multiple binary columns, increasing the feature count.
D
Regression analysis
Why wrong: Regression analysis models the relationship between dependent and independent variables; it is not a technique for feature reduction.
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.
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.
✗
Normalization
Why it's wrong here
Normalization scales features to a common range but does not reduce the number of features.
✓
Principal Component Analysis (PCA)
Why this is correct
PCA summarizes data by creating new uncorrelated variables (principal components) that capture most of the variance, effectively reducing dimensionality.
Related concept
Read the scenario before looking for a memorised answer.
✗
One-hot encoding
Why it's wrong here
One-hot encoding converts categorical variables into multiple binary columns, increasing the feature count.
✗
Regression analysis
Why it's wrong here
Regression analysis models the relationship between dependent and independent variables; it is not a technique for feature reduction.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that 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.
Detailed technical explanation
How to think about this question
PCA works by computing the covariance matrix of the data, then performing eigendecomposition to find eigenvectors (principal components) and eigenvalues that indicate the variance explained by each component. In practice, a common subtlety is that PCA assumes linear relationships and is sensitive to feature scaling, so features should be standardized before applying PCA to avoid components being dominated by high-variance features. For a retail churn dataset with dozens of correlated attributes, PCA can reduce the feature space to 5-10 components capturing 90%+ of the variance, enabling faster model training and reduced overfitting.
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: 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.
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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