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 is training a regression model to predict energy consumption. The dataset includes features like temperature, humidity, time of day, and day of week. After training, the model performs well on the training set but poorly on new data. Which approach would most likely help reduce this problem?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue: "most likely"
Why it matters: Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.
Why wrong: Adding more features can increase model complexity and exacerbate overfitting, making the problem worse.
B
Use a simpler model with fewer parameters.
A simpler model has less capacity to memorize noise, which reduces overfitting and improves generalization to new data.
C
Increase the number of training epochs.
Why wrong: Increasing epochs often allows the model to fit the training data even more precisely, worsening overfitting if not combined with regularization.
D
Use a more complex model to capture more patterns.
Why wrong: A more complex model is even more prone to overfitting, especially with a limited amount of training data.
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Use a simpler model with fewer parameters.
The model performs well on the training set but poorly on new data, which is classic overfitting. Using a simpler model with fewer parameters reduces the model's capacity to memorize noise and irrelevant patterns, forcing it to learn the underlying generalizable relationships. This directly addresses the variance problem without requiring additional data or computational resources.
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.
✗
Add more features to the model.
Why it's wrong here
Adding more features can increase model complexity and exacerbate overfitting, making the problem worse.
✓
Use a simpler model with fewer parameters.
Why this is correct
A simpler model has less capacity to memorize noise, which reduces overfitting and improves generalization to new data.
Clue confirmation
The clue word "most likely" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
✗
Increase the number of training epochs.
Why it's wrong here
Increasing epochs often allows the model to fit the training data even more precisely, worsening overfitting if not combined with regularization.
✗
Use a more complex model to capture more patterns.
Why it's wrong here
A more complex model is even more prone to overfitting, especially with a limited amount of training data.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often confuse 'poor performance on new data' with underfitting and incorrectly choose to add more features or increase complexity, when the symptom of high training accuracy with low test accuracy clearly indicates overfitting requiring simplification.
Detailed technical explanation
How to think about this question
Overfitting occurs when a model learns the training data's noise rather than the signal, often indicated by a large gap between training and validation error. Regularization techniques (e.g., L1/L2) or reducing model complexity (e.g., lowering polynomial degree in regression) directly control the bias-variance tradeoff. In Azure Machine Learning, this can be addressed by using simpler algorithms like linear regression instead of a deep neural network, or by applying automated hyperparameter tuning to find the optimal complexity.
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: Use a simpler model with fewer parameters. — The model performs well on the training set but poorly on new data, which is classic overfitting. Using a simpler model with fewer parameters reduces the model's capacity to memorize noise and irrelevant patterns, forcing it to learn the underlying generalizable relationships. This directly addresses the variance problem without requiring additional data or computational resources.
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.
Are there clue words in this question I should notice?
Yes — watch for: "most likely". Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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.