- A
Precision
Why wrong: Precision measures the proportion of positive identifications that were actually correct. While useful, it does not consider false negatives, so it can still be high if the model rarely predicts the minority class, as in this scenario.
- B
Recall
Why wrong: Recall measures the proportion of actual positives that were identified correctly. In this case, the model has zero recall for the dog class because it never predicts 'dog', so recall alone would be 0 for dogs but 100% for cats, giving an incomplete picture.
- C
F1-score
The F1-score combines precision and recall into a single metric that penalizes extreme values. For this model, the F1-score for the minority class (dogs) would be very low, revealing the poor performance that accuracy hides.
- D
Accuracy
Why wrong: Accuracy is the ratio of correct predictions to total predictions. In this highly imbalanced dataset, the model achieves 95% accuracy just by always guessing the majority class, which does not reflect its inability to detect dogs.
Quick Answer
The answer is the F1-score, which is the correct metric to prioritize for imbalanced classification. This metric combines precision and recall into a single harmonic mean, making it far more reliable than accuracy when one class significantly outnumbers another. In the cat and dog scenario, the model’s 98% accuracy is misleading because it simply predicts the majority class every time, achieving zero recall on the minority class (dogs). The F1-score penalizes this failure by dropping sharply, revealing the model’s true inability to distinguish between classes. On the Microsoft Azure AI Fundamentals AI-900 exam, this concept tests your understanding of evaluation metrics for imbalanced datasets, a common trap where high accuracy hides poor performance. A key memory tip: think of F1 as the “balance beam” that forces both precision and recall to be high—if either is low, the score falls.
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 trains a binary classification model to distinguish between images of cats and dogs. On the test set, the model achieves 98% accuracy, but a deeper inspection reveals that the test set contains 95% cats and 5% dogs, and the model predicts 'cat' for every single image. Which metric should the data scientist prioritize to get a more realistic evaluation of the model's performance on this imbalanced dataset?
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
F1-score
The F1-score is the harmonic mean of precision and recall, providing a single metric that balances both when classes are imbalanced. In this scenario, accuracy is misleadingly high (98%) because the model always predicts the majority class (cat), achieving high accuracy without actually learning to distinguish cats from dogs. The F1-score penalizes the model for its poor recall on the minority class (dogs), giving a more realistic evaluation of its 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.
- ✗
Precision
Why it's wrong here
Precision measures the proportion of positive identifications that were actually correct. While useful, it does not consider false negatives, so it can still be high if the model rarely predicts the minority class, as in this scenario.
- ✗
Recall
Why it's wrong here
Recall measures the proportion of actual positives that were identified correctly. In this case, the model has zero recall for the dog class because it never predicts 'dog', so recall alone would be 0 for dogs but 100% for cats, giving an incomplete picture.
- ✓
F1-score
Why this is correct
The F1-score combines precision and recall into a single metric that penalizes extreme values. For this model, the F1-score for the minority class (dogs) would be very low, revealing the poor performance that accuracy hides.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Accuracy
Why it's wrong here
Accuracy is the ratio of correct predictions to total predictions. In this highly imbalanced dataset, the model achieves 95% accuracy just by always guessing the majority class, which does not reflect its inability to detect dogs.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates see 98% accuracy and assume the model is performing well, failing to recognize that accuracy is meaningless on imbalanced datasets where the model can achieve high accuracy by simply predicting the majority class.
Trap categories for this question
Scenario analysis trap
Precision measures the proportion of positive identifications that were actually correct. While useful, it does not consider false negatives, so it can still be high if the model rarely predicts the minority class, as in this scenario.
Detailed technical explanation
How to think about this question
The F1-score is calculated as 2 * (precision * recall) / (precision + recall), and it ranges from 0 to 1, where 1 indicates perfect precision and recall. In this case, the model's recall for dogs is 0, so the F1-score for dogs is 0, while the F1-score for cats is high (since precision and recall for cats are both 95%), but the macro or weighted F1-score would reveal the poor overall performance. Real-world scenarios like fraud detection or medical diagnosis often use F1-score because accuracy can be misleading when the event of interest is rare.
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.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
- →
Describe fundamental principles of machine learning on Azure — study guide chapter
Learn the concepts, then practise the questions
- →
Describe fundamental principles of machine learning on Azure practice questions
Targeted practice on this topic area only
- →
All AI-900 questions
1,020 questions across all exam domains
- →
Microsoft Azure AI Fundamentals AI-900 study guide
Full concept coverage aligned to exam objectives
- →
AI-900 practice test guide
How to use practice tests most effectively before exam day
Related practice questions
Related AI-900 practice-question pages
Use these pages to review the topic behind this question. This is how one missed question becomes focused revision.
Describe Artificial Intelligence workloads and considerations practice questions
Practise AI-900 questions linked to Describe Artificial Intelligence workloads and considerations.
Describe fundamental principles of machine learning on Azure practice questions
Practise AI-900 questions linked to Describe fundamental principles of machine learning on Azure.
Describe features of computer vision workloads on Azure practice questions
Practise AI-900 questions linked to Describe features of computer vision workloads on Azure.
Describe features of Natural Language Processing workloads on Azure practice questions
Practise AI-900 questions linked to Describe features of Natural Language Processing workloads on Azure.
Describe features of generative AI workloads on Azure practice questions
Practise AI-900 questions linked to Describe features of generative AI workloads on Azure.
AI-900 fundamentals practice questions
Practise AI-900 questions linked to AI-900 fundamentals.
AI-900 scenario practice questions
Practise AI-900 questions linked to AI-900 scenario.
AI-900 troubleshooting practice questions
Practise AI-900 questions linked to AI-900 troubleshooting.
Practice this exam
Start a free AI-900 practice session
Short sessions build daily habit. Longer sessions build exam-day stamina. Try a timed session to simulate real conditions.
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: F1-score — The F1-score is the harmonic mean of precision and recall, providing a single metric that balances both when classes are imbalanced. In this scenario, accuracy is misleadingly high (98%) because the model always predicts the majority class (cat), achieving high accuracy without actually learning to distinguish cats from dogs. The F1-score penalizes the model for its poor recall on the minority class (dogs), giving a more realistic evaluation of its 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.
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 →
Same concept, more angles
4 more ways this is tested on AI-900
These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.
Variation 1. A data scientist is building a binary classification model to predict fraudulent credit card transactions. The dataset is highly imbalanced: only 1% of transactions are fraudulent. The cost of a false negative is very high because missing a fraudulent transaction can lead to significant financial loss. Which evaluation metric should the data scientist prioritize to minimize false negatives?
medium- A.Accuracy
- B.Precision
- ✓ C.Recall
- D.F1 Score
Why C: Recall (also known as sensitivity or true positive rate) measures the proportion of actual positive cases (fraudulent transactions) that are correctly identified. In this highly imbalanced scenario where missing a fraud (false negative) is extremely costly, maximizing recall ensures that the model catches as many fraudulent transactions as possible, even if it means some false positives occur. This directly aligns with the goal of minimizing false negatives.
Variation 2. A data scientist has trained a binary classification model to detect fraudulent credit card transactions. The dataset contains 99.9% legitimate transactions and only 0.1% fraudulent ones. The model predicts all transactions as legitimate, achieving 99.9% accuracy on the test set. However, the business requires the model to actually catch as many fraudulent transactions as possible. Which metric would best reveal the model's failure to identify fraud?
medium- A.Accuracy
- ✓ B.Recall
- C.Precision
- D.F1 score
Why B: Recall (also known as sensitivity) measures the proportion of actual positive cases (fraudulent transactions) that were correctly identified by the model. In this scenario, the model predicts all transactions as legitimate, so it correctly identifies 0 out of the 0.1% fraudulent transactions, yielding a recall of 0%. This directly reveals the model's complete failure to catch fraud, despite the high accuracy.
Variation 3. A data scientist is training a binary classification model to detect fraudulent transactions. The dataset contains only 1% fraudulent transactions. The model achieves 99% accuracy on the test set, but when deployed, it fails to detect most actual fraud cases. Which metric would best reveal this issue?
hard- A.Accuracy
- B.Precision
- ✓ C.Recall
- D.F1 score
Why C: Recall (sensitivity) measures the proportion of actual positive cases correctly identified. In this highly imbalanced dataset (1% fraud), a model can achieve 99% accuracy by simply predicting 'non-fraud' for every transaction, which yields zero true positives. Recall reveals this failure because it focuses solely on how many fraudulent transactions were caught, ignoring the vast majority of non-fraud cases.
Variation 4. A data scientist trains a binary classification model to detect fraudulent credit card transactions. The dataset contains 99.5% legitimate transactions and 0.5% fraudulent transactions. The model predicts every transaction as legitimate and achieves 99.5% accuracy on the test set. Which metric would best reveal that the model is failing to identify any fraudulent transactions?
medium- A.Precision
- ✓ B.Recall
- C.F1 score
- D.Mean Absolute Error (MAE)
Why B: Recall (also known as sensitivity) measures the proportion of actual positive cases correctly identified by the model. In this scenario, the model predicts all transactions as legitimate, so it correctly identifies zero fraudulent transactions, giving a recall of 0%. Accuracy alone is misleading because the dataset is highly imbalanced (99.5% legitimate, 0.5% fraudulent), and a 99.5% accuracy can be achieved by simply predicting the majority class. Recall directly reveals the model's failure to detect any fraud.
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.
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.