- A
Normalizing input data to a standard scale before training
Why wrong: Normalizing input data is data preprocessing (feature scaling) — regularization adds complexity penalties to the training loss function.
- B
Adding a complexity penalty to the training objective to reduce overfitting
Regularization (L1/L2) penalizes large model weights during training, encouraging simpler models that generalize better.
- C
Ensuring models comply with AI regulations in different jurisdictions
Why wrong: Regulatory compliance is legal governance — regularization is a mathematical technique to prevent overfitting.
- D
Standardizing the format of training data from different sources
Why wrong: Data format standardization is data engineering — regularization is an optimization technique for controlling model complexity.
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. Match the stated requirement to the specific cloud service, access model, or configuration option — many options are valid in isolation but not for this scenario. 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.
What is 'regularization' in machine learning and why is it used?
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
Adding a complexity penalty to the training objective to reduce overfitting
Regularization is a technique used to reduce overfitting by adding a penalty term to the loss function during training. This penalty discourages the model from learning overly complex patterns (e.g., large weights) that fit the training data too closely but fail to generalize to new data. In Azure Machine Learning, regularization can be applied via algorithms like Lasso (L1) or Ridge (L2) regression, which directly modify the optimization objective.
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.
- ✗
Normalizing input data to a standard scale before training
Why it's wrong here
Normalizing input data is data preprocessing (feature scaling) — regularization adds complexity penalties to the training loss function.
- ✓
Adding a complexity penalty to the training objective to reduce overfitting
Why this is correct
Regularization (L1/L2) penalizes large model weights during training, encouraging simpler models that generalize better.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Ensuring models comply with AI regulations in different jurisdictions
Why it's wrong here
Regulatory compliance is legal governance — regularization is a mathematical technique to prevent overfitting.
- ✗
Standardizing the format of training data from different sources
Why it's wrong here
Data format standardization is data engineering — regularization is an optimization technique for controlling model complexity.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates confuse regularization with data normalization or standardization, because both involve 'regularizing' data in a colloquial sense, but regularization is a penalty on model complexity, not a data transformation step.
Detailed technical explanation
How to think about this question
Under the hood, regularization works by adding a term like λ * Σ|w| (L1) or λ * Σw² (L2) to the loss function, where λ controls the penalty strength. L1 regularization can drive some weights to zero, effectively performing feature selection, while L2 shrinks weights proportionally. In Azure ML, you can configure regularization parameters in estimators like `LogisticRegression` or `SGDClassifier` to balance bias and variance, which is critical when dealing with high-dimensional datasets like text or genomics.
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 media company stores terabytes of video archives that are accessed once a year for audit purposes. Moving these objects to a cold storage tier (Azure Archive, S3 Glacier, or Google Nearline) costs a fraction of hot storage. Questions like this test whether you understand storage tiers, access frequency tradeoffs, and retrieval latency requirements.
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: Adding a complexity penalty to the training objective to reduce overfitting — Regularization is a technique used to reduce overfitting by adding a penalty term to the loss function during training. This penalty discourages the model from learning overly complex patterns (e.g., large weights) that fit the training data too closely but fail to generalize to new data. In Azure Machine Learning, regularization can be applied via algorithms like Lasso (L1) or Ridge (L2) regression, which directly modify the optimization objective.
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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