- A
How quickly the model makes predictions
Why wrong: Prediction speed is inference latency — accuracy measures the proportion of correct predictions.
- B
The proportion of correct predictions out of total predictions
Accuracy = correct predictions / total predictions. It measures overall classification correctness.
- C
How much memory the model uses during inference
Why wrong: Memory usage is a resource consumption metric — accuracy measures prediction correctness.
- D
The number of training examples used to build the model
Why wrong: Training data size is a dataset characteristic — accuracy evaluates the model's predictive performance.
Question 494 of 1,020
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 does 'model accuracy' measure in machine learning classification?
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
✓
The proportion of correct predictions out of total predictions
Model accuracy in classification measures the ratio of correctly predicted instances to the total number of predictions made. It is calculated as (True Positives + True Negatives) / (Total Predictions). This metric is fundamental in evaluating classification models on Azure Machine Learning, where it is reported in the model evaluation metrics.
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.
- ✗
How quickly the model makes predictions
Why it's wrong here
Prediction speed is inference latency — accuracy measures the proportion of correct predictions.
- ✓
The proportion of correct predictions out of total predictions
Why this is correct
Accuracy = correct predictions / total predictions. It measures overall classification correctness.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
How much memory the model uses during inference
Why it's wrong here
Memory usage is a resource consumption metric — accuracy measures prediction correctness.
- ✗
The number of training examples used to build the model
Why it's wrong here
Training data size is a dataset characteristic — accuracy evaluates the model's predictive performance.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often confuse model accuracy with performance metrics like speed or resource usage, or assume it relates to training data size, when in fact accuracy strictly measures the proportion of correct predictions.
Detailed technical explanation
How to think about this question
Under the hood, accuracy is computed by comparing the model's predicted class labels against the ground truth labels in a test dataset. In Azure Machine Learning, the 'accuracy' metric is automatically calculated during evaluation runs and can be misleading for imbalanced datasets, where a model predicting only the majority class can achieve high accuracy despite poor performance on minority classes. This is why Azure ML also provides precision, recall, and F1-score for a more nuanced view.
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
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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: The proportion of correct predictions out of total predictions — Model accuracy in classification measures the ratio of correctly predicted instances to the total number of predictions made. It is calculated as (True Positives + True Negatives) / (Total Predictions). This metric is fundamental in evaluating classification models on Azure Machine Learning, where it is reported in the model evaluation metrics.
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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