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. A key principle to apply: regularization adds a penalty to the loss function during training.. 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 trains a regression model to predict house prices using features like square footage, number of bedrooms, and location. The model achieves very high accuracy on the training data but performs poorly on a held-out test set. Which technique should the data scientist apply to reduce overfitting?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Use regularization
Regularization (Option C) is the correct technique to reduce overfitting because it adds a penalty term to the loss function (e.g., L1 or L2 regularization), which discourages the model from learning overly complex patterns that fit noise in the training data. This helps the model generalize better to unseen data, such as the held-out test set, by constraining the magnitude of feature weights.
Key principle: Regularization adds a penalty to the loss function during training.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
✗
Increase the number of features
Why it's wrong here
Adding more features increases model complexity, which often worsens overfitting.
✗
Decrease the training data size
Why it's wrong here
Having less training data generally increases the risk of overfitting, as the model has fewer examples to generalize from.
✓
Use regularization
Why this is correct
Regularization (e.g., L1 or L2) penalizes large coefficients, simplifying the model and reducing overfitting.
Related concept
Regularization adds a penalty to the loss function during training.
✗
Increase the number of training epochs
Why it's wrong here
More epochs can lead to further overfitting as the model becomes more specialized to the training data.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often think adding more data or features always improves performance, but in overfitting scenarios, regularization directly addresses the core issue of model complexity, while increasing features or epochs typically worsens it.
Detailed technical explanation
How to think about this question
Under the hood, regularization techniques like L2 (Ridge) add a squared magnitude of coefficients as a penalty term, effectively shrinking weights toward zero and reducing model complexity. In Azure Machine Learning, regularization can be applied via hyperparameter tuning using algorithms like Lasso or ElasticNet, and the regularization strength (alpha) is a critical parameter to balance bias and variance. A real-world scenario is predicting house prices with many correlated features (e.g., square footage and number of rooms), where regularization helps prevent the model from assigning extreme importance to redundant or noisy features.
KKey Concepts to Remember
Regularization adds a penalty to the loss function during training.
L1 (Lasso) and L2 (Ridge) are common types of regularization.
It discourages large coefficients, simplifying the model.
Regularization improves model generalization to unseen data.
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
Regularization adds a penalty to the loss function during training.
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. Regularization adds a penalty to the loss function during training. 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.
Review regularization adds a penalty to the loss function during training., then practise related AI-900 questions on the same topic to reinforce the concept.
Describe fundamental principles of machine learning on Azure — This question tests Describe fundamental principles of machine learning on Azure — Regularization adds a penalty to the loss function during training..
What is the correct answer to this question?
The correct answer is: Use regularization — Regularization (Option C) is the correct technique to reduce overfitting because it adds a penalty term to the loss function (e.g., L1 or L2 regularization), which discourages the model from learning overly complex patterns that fit noise in the training data. This helps the model generalize better to unseen data, such as the held-out test set, by constraining the magnitude of feature weights.
What should I do if I get this AI-900 question wrong?
Review regularization adds a penalty to the loss function during training., then practise related AI-900 questions on the same topic to reinforce the concept.
What is the key concept behind this question?
Regularization adds a penalty to the loss function during training.
About these practice questions
Courseiva creates original exam-style practice questions with explanations and wrong-answer analysis. It does not publish real exam questions, exam dumps, or protected exam content. Learn why practice questions differ from exam dumps →
Share a tip, memory trick, or ask about the reasoning behind this question. Do not post real exam questions, leaked content, braindumps, or copyrighted exam material. Comments are moderated and may be removed without notice.
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.
Question Discussion
Share a tip, memory trick, or ask about the reasoning behind this question. Do not post real exam questions, leaked content, braindumps, or copyrighted exam material. Comments are moderated and may be removed without notice.
Sign in to join the discussion.