- A
Overfitting to the training data
Why wrong: Overfitting to training data would cause high training accuracy but low test accuracy, but the tuning process described uses the test set directly, not the training set.
- B
Data leakage from the training set to the test set
Why wrong: Data leakage typically occurs when information from outside the training set influences the model during training, e.g., future data used to predict the past. This scenario does not describe such leakage.
- C
Overfitting to the test set
Correct. The model's hyperparameters were tuned based on test set performance, causing the model to perform well on that specific test set but poorly on new data. This is overfitting to the test set.
- D
Underfitting the training data
Why wrong: Underfitting would result in poor performance on both training and test sets, which is not the case here as the model achieved 95% test accuracy.
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. The scenario asks you to isolate a root cause — eliminate options that address a different problem before choosing. 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 developing a classification model to detect fraudulent transactions. The dataset is split into training and test sets. The data scientist repeatedly tunes the model's hyperparameters and evaluates performance on the test set until the test accuracy reaches 95%. However, when the model is deployed on new, unseen data, its accuracy drops to 70%. Which concept best explains this performance degradation?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue:
"best"Why it matters: Signals that multiple options may be partially correct. Choose the option that most directly solves the exact problem described, not the one that sounds most complete.
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
Overfitting to the test set
Option C is correct because the data scientist repeatedly tuned hyperparameters based on test set performance, effectively using the test set as part of the training process. This causes the model to become specialized to the test set's specific patterns and noise, so it fails to generalize to new, unseen data. This phenomenon is known as overfitting to the test set, where the test set no longer provides an unbiased estimate of real-world performance.
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.
- ✗
Overfitting to the training data
Why it's wrong here
Overfitting to training data would cause high training accuracy but low test accuracy, but the tuning process described uses the test set directly, not the training set.
- ✗
Data leakage from the training set to the test set
Why it's wrong here
Data leakage typically occurs when information from outside the training set influences the model during training, e.g., future data used to predict the past. This scenario does not describe such leakage.
- ✓
Overfitting to the test set
Why this is correct
Correct. The model's hyperparameters were tuned based on test set performance, causing the model to perform well on that specific test set but poorly on new data. This is overfitting to the test set.
Clue confirmation
The clue word "best" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Underfitting the training data
Why it's wrong here
Underfitting would result in poor performance on both training and test sets, which is not the case here as the model achieved 95% test accuracy.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates confuse overfitting to the training data with overfitting to the test set, failing to recognize that repeatedly evaluating on the test set can cause the model to memorize test set patterns rather than generalize.
Trap categories for this question
Scenario analysis trap
Data leakage typically occurs when information from outside the training set influences the model during training, e.g., future data used to predict the past. This scenario does not describe such leakage.
Detailed technical explanation
How to think about this question
When a test set is used repeatedly for hyperparameter tuning, the model's parameters can indirectly learn from the test set's distribution, violating the assumption that the test set is an independent holdout. This is a form of adaptive overfitting, where the model's performance on the test set becomes an optimistic estimate. In real-world scenarios, this is avoided by using a separate validation set for tuning and reserving the test set for a single final evaluation.
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.
What to study next
Got this wrong? Here's your next step.
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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: Overfitting to the test set — Option C is correct because the data scientist repeatedly tuned hyperparameters based on test set performance, effectively using the test set as part of the training process. This causes the model to become specialized to the test set's specific patterns and noise, so it fails to generalize to new, unseen data. This phenomenon is known as overfitting to the test set, where the test set no longer provides an unbiased estimate of real-world performance.
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: "best". Signals that multiple options may be partially correct. Choose the option that most directly solves the exact problem described, not the one that sounds most complete.
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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